Here, the average value is subtracted from each acceleration component beforehand. The normalized cross correlation characterizes the similarity between two signal segments (therefore, the rhythmicity of the signal), offering the basis of self-adaptive algorithm of automatic gait peak detection [41–44]. Moreover, this value is a robust measure because it is rather insensitive to the manner of setting the coordinate system. This feature is especially useful in the analysis of long-term acceleration data, since the position or orientation of the device might be altered from the predefined one during the day; for example, it sometimes occurs that the device is worn upside down and/or back to front after the patient changes clothes.

We also paid attention to another important feature of gait: biphasicity vs. monophasicity. This means that, during one gait cycle, both vertical and anterior/posterior accelerations change in a biphasic manner, that is, a similar pattern is repeated twice per cycle, while medial/lateral acceleration shows a monophasic pattern. The biphasicity/monophasicity inherent in human gait helps to identify correct stride peaks with high precision as described below.

• From the initial *t*0

denoted as *T*2

• The region between *T*2

• Selection of a point *t*

tion signal [42].

peak candidates.

*Dz*

*2.2.4. Detection of stride peaks*

.

with a cycle duration of <2 sec.

continuously walking regions.

*2.2.3. Estimation of stride peak candidates*

following template-matching algorithm:

ments centered at other LMP.

of those correlation coefficients.

0

are repeated backward in time, that is, 0 ≤ *t*0

and *T*1

and the template selected in procedure 2, procedures (3), (4), and (5)

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily…

from LMP and generation of a template wave with *p* consecutive

rhythm block characterized by highly rhythmic behavior of moderate or large intensity

• Procedures (2)–(7) are repeated. In this way, the entire time series is divided into active rhythm blocks and inactive and/or nonrhythmic blocks. The former can be regarded as

From each of the continuously walking regions, stride peak candidates are estimated by the

points centered at this point. The template size *p* can be typically set to 50, but more precisely, it should be optimized based on the characteristic timescale of the original accelera-

• Calculation of the normalized cross correlation between the template and all signal seg-

• Selection of the segments with correlation coefficient > 0.5. Calculation of the average value

• Procedures (1)–(3) are repeated for all LMP. The template wave that yields the largest aver-

• Calculation of the normalized cross correlation time series by sliding the optimized tem-

• Extraction of the local maximum points from the normalized cross correlation time series. Among these points, the ones with positive correlation coefficient are the desired stride

The candidate peaks obtained so far are not fiducial points due to the possible presence of extra peaks related to the contact of both feet (step peaks) and other spurious points. Therefore, stride peaks are determined with the help of a "guide wave" by the following procedure:

tion with high-pass filtering to avoid integration drift. The integrated signal [*Dx*

(*t*) *Ay*

(*t*)] corresponds to the relative displacement of the body where the device was attached.

(*t*) *Az*

age correlation coefficient is chosen as the optimized template.

Hereafter, the time index of the optimized template center is indicated by *t*

plate over the current continuously walking region.

• The three-dimensional acceleration signal [*Ax*

is extracted from the acceleration signal. This is an active

–*t* ≤ 2. The central time of the final template is

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*c* .

(*t*)] is integrated twice in combina-

(*t*) *Dy* (*t*)

**Figure 2.** Diagram of the algorithm using the pattern-matching method and analysis of the relationship between gait force and rhythm.

#### *2.2.2. Estimation of continuously walking region*

Possible walking regions with stride cycle <2 sec are estimated from the whole time series by the following procedure:

### *2.2.3. Estimation of stride peak candidates*

From each of the continuously walking regions, stride peak candidates are estimated by the following template-matching algorithm:

Hereafter, the time index of the optimized template center is indicated by *t c* .

#### *2.2.4. Detection of stride peaks*

*2.2.2. Estimation of continuously walking region*

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *Ax* (*t*)2 + *Ay* (*t*)2 + *Az* (*t*)2

.

the following procedure:

• Total acceleration *Ar*

equation:

force and rhythm.

30 Wearable Technologies

• *Ar*(*t*) = √

1.5 m/s2

LMP, [*Ax*

(*t*0 ) *Ay* (*t*0 ) *Az* (*t*0

of 0.5 sec when Δ*t* = 10 msec.

ments centered at other LMP, [*Ax*

Possible walking regions with stride cycle <2 sec are estimated from the whole time series by

**Figure 2.** Diagram of the algorithm using the pattern-matching method and analysis of the relationship between gait

• From this time series, local maximum points above a certain threshold are picked up. The time indices of these points are hereafter referred to as LMP. The threshold value is typically set to

• Selection of a template wave is composed of *p* consecutive points centered at one of those

• Calculation of the normalized cross correlation between the template and all signal seg-

• Among the segments with correlation coefficient > 0.5, a segment that yields the highest correlation coefficient is selected. The template wave is replaced with this segment wave. The existence of a segment with large correlation coefficient (>0.5) strongly suggests that

• Procedures (3) and (4) are repeated until no segment waves with correlation coefficient > 0.5

are found. The central time of the final template thus obtained is denoted as *T*1

(*t*) *Az*

(*t*) *Ay*

the signal within the current time range is highly periodic.

to ensure that other cyclic events with low intensity, such as tremor, can be excluded.

(*t*)] for 0 ≤ *t*-*t*

(*t*) is calculated from the filtered acceleration components by the

)]. Typically, *p* is chosen as 50, which corresponds to a time interval

0 ≤ 2 (sec).

.

The candidate peaks obtained so far are not fiducial points due to the possible presence of extra peaks related to the contact of both feet (step peaks) and other spurious points. Therefore, stride peaks are determined with the help of a "guide wave" by the following procedure:

• The three-dimensional acceleration signal [*Ax* (*t*) *Ay* (*t*) *Az* (*t*)] is integrated twice in combination with high-pass filtering to avoid integration drift. The integrated signal [*Dx* (*t*) *Dy* (*t*) *Dz* (*t*)] corresponds to the relative displacement of the body where the device was attached. This operation works to smooth the original signal and enhances the monophasic nature of medial/lateral movements during walking.

120, 132, and 138 beats per minute. The subjects were asked to contact the right or left foot corresponding to the beat. Thus, the expected gait cycle (the duration from the ground contact of one foot to the next contact of the same foot) was 1.30, 1.20, 1.07, 1.00, 0.91, and 0.87 sec, respectively. In this control study, subjects equipped with the PGR device walked on the force plate (WalkWay MW-1000, Anima, Tokyo). Since the force plate was 2.4 m long, only part of the metronomeguided 10 m walking was recorded simultaneously by the two devices. The gait cycles recorded by the two equipments were compared across all subjects and all metronomic paces by using the Bland–Altman plot and intraclass correlation (ICC (2,1)) methods. The subjects were asked to walk in sync with the beats of the metronome. The cycle of the metronome-guided walking was set at five stages: 1.30, 1.20, 1.07, 1.00, 0.91, and 0.87 sec. **Figure 3** shows an example of the recorded accelerations by PGR during the metronome recording. **Figure 3A** shows an example where a difference was observed between the expected and recorded gait cycles. The expected cycle was 0.87 sec, whereas the recorded cycle was 0.94 sec. On the other hand, **Figure 3B** shows

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**Table 2** shows a summary of the gait cycles recorded by the PGR and the force plate, for each subject. We could not identify the gait cycle from the force plate data in four cases out of the total of 36 measurements. Therefore, we used 32 pairs of data for comparison of the two methods. **Figure 3C** shows the Bland–Altman plot, demonstrating the difference in two gait cycles as a function of their mean values. The 95% confidence interval of the mean difference was [−0.0191 to 0.0003], which includes zero, and no significant regression was observed (*p* = 0.49),

**Figure 3.** (A, B) Examples of gait-induced accelerations during metronome-guided walking. The expected rate defined by the metronome was 0.87 sec in (A) and 1.00 sec in (B). (C) Bland–Altman plot of two gait cycles estimated by force plate and PGR. The differences between the two sets of gait cycles are plotted against their mean values. The solid line

represents the mean difference, while the dashed lines are the 95% confidence limit lines (bias ± 1.96 SD).

an example where the recorded gait cycle matched the expected gait cycle.

The method of making a guide wave is not limited to the one presented here. The key is to find a suitable monophasic signal, that is, a signal composed of a wavelet that is repeated once per single gait cycle. In [42], we designed a different type of guide wave by utilizing the anisotropic character of the *x*-axis acceleration signal.

**Figure 1B** and **C** illustrates the validity of this procedure. **Figure 1B** shows an example of the total acceleration wave. The center point of the optimized template is marked by an open circle. The detected peaks using the guide wave are indicated by asterisks in **Figure 1C**. It is clear that only the stride peaks, that is, gait peaks associated with the same leg, are selected, while peaks due to the other leg are not counted. In our earlier work, we found that this type of peak detection algorithm performs fairly well for the gait of PD patients under supervised but non-laboratory conditions, with more than 95% accuracy [43].

#### *2.2.5. Estimation of "gait cycle duration" and "gait-induced acceleration"*

After identifying correct stride peaks, the duration of a single gait cycle and the amplitude of gait-induced acceleration per cycle are calculated as follows. Let two adjacent stride peaks be indexed as *i* = 1 and *i* = *N*. Then, the gait cycle duration is given by (*N* − 1)Δ*t*, and gait acceleration is defined by ∑*i*=1 *N*−1 *A*r (*i*Δ*t*) \_\_\_\_\_\_\_\_ *N* − 1 . These measures are calculated from all peak-to-peak intervals. Since gait accelerations correlate with floor reaction forces, the amplitude of gait acceleration is selected as an index of floor reaction force. Thus, the obtained gait cycle duration and the amplitude of gait-related acceleration are averaged for every 10-min period of recording.

#### *2.2.6. Accuracy of analysis using pattern-matching method in detecting gait-induced signals*

To further assess the sensitivity of the PGR, we compared the results of PGR with those of a standard system using floor reaction forces [unpublished data]. We asked the subject to step in response to a particular gait cycle determined by a metronome (0.87–1.30 sec) and compared the reappearance of the cycle recorded from the two devices.

Using the PGR, we recorded metronome-guided 10 m walking in six normal control subjects (age, 43.3 ± 7.7 years, mean ± SD, four men, and two women). The metronome was set at 92, 100, 112, 120, 132, and 138 beats per minute. The subjects were asked to contact the right or left foot corresponding to the beat. Thus, the expected gait cycle (the duration from the ground contact of one foot to the next contact of the same foot) was 1.30, 1.20, 1.07, 1.00, 0.91, and 0.87 sec, respectively.

This operation works to smooth the original signal and enhances the monophasic nature of

ner product is time-dependent, giving a new time series, which is called here "guide wave." Here, the time range of the unit vector *w* is decided so that the obtained guide wave behaves like a square wave with the same monophasic pattern as the medial/lateral (*x*-axis) signal. Usually, it is almost equal to the time range of the optimized template wave, that is, 2*w* = *p*. • Selection of the candidate stride peak with the largest correlation coefficient per single

The method of making a guide wave is not limited to the one presented here. The key is to find a suitable monophasic signal, that is, a signal composed of a wavelet that is repeated once per single gait cycle. In [42], we designed a different type of guide wave by utilizing the

**Figure 1B** and **C** illustrates the validity of this procedure. **Figure 1B** shows an example of the total acceleration wave. The center point of the optimized template is marked by an open circle. The detected peaks using the guide wave are indicated by asterisks in **Figure 1C**. It is clear that only the stride peaks, that is, gait peaks associated with the same leg, are selected, while peaks due to the other leg are not counted. In our earlier work, we found that this type of peak detection algorithm performs fairly well for the gait of PD patients under supervised

After identifying correct stride peaks, the duration of a single gait cycle and the amplitude of gait-induced acceleration per cycle are calculated as follows. Let two adjacent stride peaks be indexed as *i* = 1 and *i* = *N*. Then, the gait cycle duration is given by (*N* − 1)Δ*t*, and gait accel-

Since gait accelerations correlate with floor reaction forces, the amplitude of gait acceleration is selected as an index of floor reaction force. Thus, the obtained gait cycle duration and the amplitude of gait-related acceleration are averaged for every 10-min period of recording.

To further assess the sensitivity of the PGR, we compared the results of PGR with those of a standard system using floor reaction forces [unpublished data]. We asked the subject to step in response to a particular gait cycle determined by a metronome (0.87–1.30 sec) and com-

Using the PGR, we recorded metronome-guided 10 m walking in six normal control subjects (age, 43.3 ± 7.7 years, mean ± SD, four men, and two women). The metronome was set at 92, 100, 112,

*2.2.6. Accuracy of analysis using pattern-matching method in detecting gait-induced signals*

*N* − 1 . These measures are calculated from all peak-to-peak intervals.

(*t* c -*w*) *Dy* (*t* c -*w*) *Dz* (*t* c

(*t* + *w*)] in 3D space. The inner product of these two vectors is calculated. The in-

(*t*-*w*) *Dz*

(*t*-*w*) *Dy*

(*t* c + *w*) *Dy* (*t* c + *w*)

(*t*-*w*)] to [*Dx*

(*t* + *w*)

medial/lateral movements during walking.

• (2) Consider a fixed unit vector pointing from [*Dx*

guide wave cycle. These are fiducial stride peaks.

anisotropic character of the *x*-axis acceleration signal.

*N*−1 *A*r (*i*Δ*t*) \_\_\_\_\_\_\_\_

but non-laboratory conditions, with more than 95% accuracy [43].

*2.2.5. Estimation of "gait cycle duration" and "gait-induced acceleration"*

pared the reappearance of the cycle recorded from the two devices.

c + *w*)] and an arbitrary unit vector pointing from [*Dx*

*Dz* (*t*

*Dy*

(*t* + *w*) *Dz*

32 Wearable Technologies

eration is defined by ∑*i*=1

In this control study, subjects equipped with the PGR device walked on the force plate (WalkWay MW-1000, Anima, Tokyo). Since the force plate was 2.4 m long, only part of the metronomeguided 10 m walking was recorded simultaneously by the two devices. The gait cycles recorded by the two equipments were compared across all subjects and all metronomic paces by using the Bland–Altman plot and intraclass correlation (ICC (2,1)) methods. The subjects were asked to walk in sync with the beats of the metronome. The cycle of the metronome-guided walking was set at five stages: 1.30, 1.20, 1.07, 1.00, 0.91, and 0.87 sec. **Figure 3** shows an example of the recorded accelerations by PGR during the metronome recording. **Figure 3A** shows an example where a difference was observed between the expected and recorded gait cycles. The expected cycle was 0.87 sec, whereas the recorded cycle was 0.94 sec. On the other hand, **Figure 3B** shows an example where the recorded gait cycle matched the expected gait cycle.

**Table 2** shows a summary of the gait cycles recorded by the PGR and the force plate, for each subject. We could not identify the gait cycle from the force plate data in four cases out of the total of 36 measurements. Therefore, we used 32 pairs of data for comparison of the two methods. **Figure 3C** shows the Bland–Altman plot, demonstrating the difference in two gait cycles as a function of their mean values. The 95% confidence interval of the mean difference was [−0.0191 to 0.0003], which includes zero, and no significant regression was observed (*p* = 0.49),

**Figure 3.** (A, B) Examples of gait-induced accelerations during metronome-guided walking. The expected rate defined by the metronome was 0.87 sec in (A) and 1.00 sec in (B). (C) Bland–Altman plot of two gait cycles estimated by force plate and PGR. The differences between the two sets of gait cycles are plotted against their mean values. The solid line represents the mean difference, while the dashed lines are the 95% confidence limit lines (bias ± 1.96 SD).

correlation between (logarithm of) gait-induced acceleration and walking speed, when these

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However, it should be noted that any gait parameter, when used separately, is more than often insufficient to allow complete understanding of ambulatory gait behavior. For example, a person usually changes gait cycle duration (or cadence) over the course of a day both voluntarily and involuntarily. Real life comprises a diversity of environmental constraints, making it almost impossible for us to keep on walking freely. Therefore, even if the gait parameter appears to deteriorate in a certain time of the day, one cannot decide whether it is caused by disorders in internal factors (e.g., the subject's physical conditions) or by changes in walking pace due to external perturbations. In most cases, such "forced" changes are transient, and their effects could be reduced by taking the long-time average of the parameter. However, the combination of gait cycle duration and gait acceleration provides a more powerful solution to this problem, as will be demonstrated in this paper and as was reported previously [43, 44]. The two measures, when plotted on a plane, will fall on a rather well-ordered structure that is individual-specific and robust against external constraints. The robustness of the gait cycleacceleration relationship is considered to result from the human tendency to automatically select preprogrammed preferred walking behavior in response to environmental changes [43]. Based on this property, we can obtain insightful information on intrinsic gait problems from ambulatory monitoring, which is the greatest merit of using the proposed gait measures.

**3. Quantification of "poverty of movements" and "slowness/**

gamma distribution is defined by the following formula:

value of normal control subjects (0.69 ± 0.19 m/sec2

*per 24 hrs* for patients with score ≥ 30 (0.31 ± 0.09 m/sec2

showed a decrease below 0.50 m/sec2

*f*(*x*) = *xk*−1 *e* −*x*/*θ*/Г(*k*) *θk* for *x* > 0

**smallness in executed movements": "amount of overall movements**

Poverty in movements and slowness/smallness in executed movements are the core motor clinical signs of PD. Overall activities should be examined to quantify these two clinical signs. In order to quantify overall activities, we analyzed the distribution of 10-min averaged various movement-induced accelerations (**Figure 4A** and **B**) and calculated the mean value of the distribution with the assumption that the curve fits with gamma distribution [45, 53]. The

The mean value was defined as the *amount of overall movements per 24 hrs*, representing an

A total of 50 patients with PD and 17 normal control subjects participated in this analysis [45]. The distribution curve shifted to the left, suggesting a decrease in activities (**Figure 4A** and **B**). The *amount of overall movements per 24 hrs* was smaller in PD patients compared with the mean

*per 24 hrs* decreased in proportion with the severity of PD. The *amount of overall movements*

). Furthermore, 38 of the 50 patients (76%)

) was significantly lower than that of

, the value of mean (1sd). The *amount of overall movements*

index of poverty of movements and slowness/smallness of executed movements.

two parameters are expressed by body height [50].

**per 24 hrs"**

**Table 2.** Recorded gait cycle during metronome-guided walking.

suggesting that there was no systematic error in the Bland-Altman plot. Moreover, the two gait cycles yielded a fairly high ICC value of 0.98 (*p* < 0.0001), with 95% confidence interval value of [0.96-0.99]. These results indicate that the recorded gait cycle by the PGR was statistically in good agreement with that of the force plate. The results showed no significant differences between the two methods, indicating that PGR accurately monitors gait movements with sensitivity comparable to that of the force plates. Since more steps were analyzed in the recordings using PGR than those using the force plate, fluctuation appears to be small. The results of the control experiments indicate that PGR can quantitatively estimate gait disorders in daily life with good sensitivity.

#### *2.2.7. Merits of proposed gait measures*

One notable merit of the developed method is that both gait cycle duration and gait-induced acceleration can serve as quantitative indices for the assessment of daily gait performance. Other basic parameters that are widely used in gait analysis are walking speed and step length. While these two parameters are not measured directly by our method, it is possible to estimate them with good accuracy from the recorded gait measures, since there is a close correlation between (logarithm of) gait-induced acceleration and walking speed, when these two parameters are expressed by body height [50].

However, it should be noted that any gait parameter, when used separately, is more than often insufficient to allow complete understanding of ambulatory gait behavior. For example, a person usually changes gait cycle duration (or cadence) over the course of a day both voluntarily and involuntarily. Real life comprises a diversity of environmental constraints, making it almost impossible for us to keep on walking freely. Therefore, even if the gait parameter appears to deteriorate in a certain time of the day, one cannot decide whether it is caused by disorders in internal factors (e.g., the subject's physical conditions) or by changes in walking pace due to external perturbations. In most cases, such "forced" changes are transient, and their effects could be reduced by taking the long-time average of the parameter. However, the combination of gait cycle duration and gait acceleration provides a more powerful solution to this problem, as will be demonstrated in this paper and as was reported previously [43, 44]. The two measures, when plotted on a plane, will fall on a rather well-ordered structure that is individual-specific and robust against external constraints. The robustness of the gait cycleacceleration relationship is considered to result from the human tendency to automatically select preprogrammed preferred walking behavior in response to environmental changes [43]. Based on this property, we can obtain insightful information on intrinsic gait problems from ambulatory monitoring, which is the greatest merit of using the proposed gait measures.

## **3. Quantification of "poverty of movements" and "slowness/ smallness in executed movements": "amount of overall movements per 24 hrs"**

suggesting that there was no systematic error in the Bland-Altman plot. Moreover, the two gait cycles yielded a fairly high ICC value of 0.98 (*p* < 0.0001), with 95% confidence interval value of [0.96-0.99]. These results indicate that the recorded gait cycle by the PGR was statistically in good agreement with that of the force plate. The results showed no significant differences between the two methods, indicating that PGR accurately monitors gait movements with sensitivity comparable to that of the force plates. Since more steps were analyzed in the recordings using PGR than those using the force plate, fluctuation appears to be small. The results of the control experiments indicate that PGR can quantitatively estimate gait disorders

Metronome (beats/min) 92 100 112 120 132 138 Expected gait cycle (sec) 1.30 1.20 1.07 1.00 0.91 0.87

 Force plate (sec) 1.10 1.08 1.08 0.98 MG-M1100 (sec) 1.18 1.08 1.05 1.04 1.01 0.94

 Force plate (sec) 1.27 1.18 1.00 0.94 0.88 0.84 MG-M1100 (sec) 1.29 1.19 1.05 0.98 0.90 0.86

MG-M1100 (sec) 1.28 1.20 1.07 1.00 0.91 0.87

 Force plate (sec) 1.30 1.20 1.06 0.96 0.89 0.87 MG-M1100 (sec) 1.30 1.20 1.07 1.00 0.91 0.87

 Force plate (sec) 1.28 1.16 1.00 0.96 0.91 0.84 MG-M1100 (sec) 1.25 1.19 1.07 1.00 0.91 0.87

 Force plate (sec) 1.20 1.08 1.02 0.90 0.84 MG-M1100 (sec) 1.26 1.20 1.06 1.02 0.91 0.87

Force plate (sec) 1.24 1.18 1.06 1.02 0.92

One notable merit of the developed method is that both gait cycle duration and gait-induced acceleration can serve as quantitative indices for the assessment of daily gait performance. Other basic parameters that are widely used in gait analysis are walking speed and step length. While these two parameters are not measured directly by our method, it is possible to estimate them with good accuracy from the recorded gait measures, since there is a close

in daily life with good sensitivity.

**Table 2.** Recorded gait cycle during metronome-guided walking.

Subject 1

34 Wearable Technologies

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

*2.2.7. Merits of proposed gait measures*

Poverty in movements and slowness/smallness in executed movements are the core motor clinical signs of PD. Overall activities should be examined to quantify these two clinical signs. In order to quantify overall activities, we analyzed the distribution of 10-min averaged various movement-induced accelerations (**Figure 4A** and **B**) and calculated the mean value of the distribution with the assumption that the curve fits with gamma distribution [45, 53]. The gamma distribution is defined by the following formula:

*f*(*x*) = *xk*−1 *e* −*x*/*θ*/Г(*k*) *θk* for *x* > 0

The mean value was defined as the *amount of overall movements per 24 hrs*, representing an index of poverty of movements and slowness/smallness of executed movements.

A total of 50 patients with PD and 17 normal control subjects participated in this analysis [45]. The distribution curve shifted to the left, suggesting a decrease in activities (**Figure 4A** and **B**). The *amount of overall movements per 24 hrs* was smaller in PD patients compared with the mean value of normal control subjects (0.69 ± 0.19 m/sec2 ). Furthermore, 38 of the 50 patients (76%) showed a decrease below 0.50 m/sec2 , the value of mean (1sd). The *amount of overall movements per 24 hrs* decreased in proportion with the severity of PD. The *amount of overall movements per 24 hrs* for patients with score ≥ 30 (0.31 ± 0.09 m/sec2 ) was significantly lower than that of

and plotted. Interestingly, a regression line can be determined for the plotted data. In **Figure 4C** and **D**, the thick black line represents the regression line of data of a PD patient, while the thin red line represents the regression line for the data of 17 normal control subjects. The data clearly show a correlation between accelerations and cycle; for example, walking with a slow cycle is associated with low amplitude of gait accelerations. The slope of the linear regression

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The *gait acceleration-cycle duration curves* of PD patients showed two distinct patterns compared to those of normal control subjects [45, 46]. First, the position of the curve in PD deviated from that of the normal control curve; the curve of PD patients was shifted to the left on the acceleration axis (**Figure 4C** and **D**), suggesting a decrease in the mean amplitude of gaitinduced acceleration. Furthermore, the curve of PD patients was shifted upward (**Figure 4C**) or downward (**Figure 4D**) on the gait cycle duration axis, suggesting prolongation or shortening of the mean gait cycle duration, respectively. Second, the range value around the regression line was narrow in PD patients [45, 46]. A narrow range of acceleration was associated with a steep slope of the regression line (**Figure 4C**), whereas a narrow range of gait cycle

Based on these findings, we next quantified these two kinds of changes in t*he gait acceleration-*

We first examined changes in the mean amplitude of gait-induced acceleration and gait cycle duration during daily walking in 64 PD patients and 17 normal control subjects [48]. The mean values of the gait-induced acceleration and gait cycle duration were computed (**Figure 5**). PD patients walked with significantly lower gait accelerations compared with normal control subjects, but there were no significant differences among the disease stage groups. In contrast, PD patients with modified Hoehn and Yahr (mH&Y) stage 1.5 walked with significantly faster gait cycle duration (faster rhythm) than those with stages 2.5–3.0 and normal control subjects, although there was no significant difference in gait cycle duration between patients with stages 2.5–3.0 PD and normal control subjects. The lower gait accelerations (i.e., inappropriate force production) can be the cause of narrow stride [42, 43]. Thus, the above results suggest that PD patients cannot walk with a sufficient stride throughout the clinical course, which can be compensated by increasing stepping [48, 55–57] at the early stage, but not at the advanced stage [48]. Our data suggest that computation of acceleration of gait movements and gait cycle duration from daily walking can be useful in the assessment of the clinical stage of the gait disorder.

*cycle duration curve*, that is, changes in the mean value and range, as described below.

**4.2. Changes in mean values of gait-induced acceleration and gait cycle duration:**

**4.3. Narrow range of gait-induced acceleration and gait cycle duration: Possible**

Another characteristic change in the shape of the *gait acceleration-cycle duration curv*e is the narrowness of the range of force and/or rhythm [45, 46]. When PD patients walked with a narrow range of change in gait acceleration (i.e., floor reaction forces), the slope of the regression line was steep (*steep type*) (**Figure 4C**). In contrast, when PD patients walked with a narrow

duration was associated with a flat slope of the regression line (**Figure 4D**).

line for data of the normal control subjects was 1.20 ± 0.29.

**Possible index of PD deficits in gait control neural circuits**

**index of monotony in PD**

**Figure 4.** (A, B) Probability distribution of the averaged acceleration per 10 min, obtained from 24 hrs of continuous recording. (C, D) examples of gait acceleration-cycle duration relationship in a patient with Parkinson's disease (PD) based on 24 hrs of gait recording. Black thick line: Corresponding regression line. In (C) and (D), red thin line: Regression line obtained from the summed average of 17 normal control subjects. The regression line of the PD patient is steep in (C), whereas it is flat in (D).

patients with scores 1–15 (0.40 ± 0.09 m/sec2 ) (*p* < 0.05) and that of patients with scores 16–30 (0.48 ± 0.09 m/sec2 ) (*p* < 0.01). Furthermore, in 50 patients with untreated PD, the *amount of overall movements per 24 hrs* was significantly associated with the UPDS part II score and part III score [53]. These results suggest that the *amount of overall movements per 24 hrs* can be a good index for poverty of movements and slowness/smallness of executed movements in ADL.

### **4. Quantitative estimation of parkinsonian gait using two parameters: "gait-induced acceleration" and "gait cycle duration"**

#### **4.1. Relationship between gait-induced acceleration and gait cycle duration**

The relationship between gait-induced acceleration and gait cycle duration (*gait accelerationcycle duration curves*) [45, 46] is shown in **Figure 4C** and **D**. Both parameters were recorded continuously for more than 24 hrs, and then the average value of every 10 min was computed and plotted. Interestingly, a regression line can be determined for the plotted data. In **Figure 4C** and **D**, the thick black line represents the regression line of data of a PD patient, while the thin red line represents the regression line for the data of 17 normal control subjects. The data clearly show a correlation between accelerations and cycle; for example, walking with a slow cycle is associated with low amplitude of gait accelerations. The slope of the linear regression line for data of the normal control subjects was 1.20 ± 0.29.

The *gait acceleration-cycle duration curves* of PD patients showed two distinct patterns compared to those of normal control subjects [45, 46]. First, the position of the curve in PD deviated from that of the normal control curve; the curve of PD patients was shifted to the left on the acceleration axis (**Figure 4C** and **D**), suggesting a decrease in the mean amplitude of gaitinduced acceleration. Furthermore, the curve of PD patients was shifted upward (**Figure 4C**) or downward (**Figure 4D**) on the gait cycle duration axis, suggesting prolongation or shortening of the mean gait cycle duration, respectively. Second, the range value around the regression line was narrow in PD patients [45, 46]. A narrow range of acceleration was associated with a steep slope of the regression line (**Figure 4C**), whereas a narrow range of gait cycle duration was associated with a flat slope of the regression line (**Figure 4D**).

Based on these findings, we next quantified these two kinds of changes in t*he gait accelerationcycle duration curve*, that is, changes in the mean value and range, as described below.

#### **4.2. Changes in mean values of gait-induced acceleration and gait cycle duration: Possible index of PD deficits in gait control neural circuits**

We first examined changes in the mean amplitude of gait-induced acceleration and gait cycle duration during daily walking in 64 PD patients and 17 normal control subjects [48]. The mean values of the gait-induced acceleration and gait cycle duration were computed (**Figure 5**). PD patients walked with significantly lower gait accelerations compared with normal control subjects, but there were no significant differences among the disease stage groups. In contrast, PD patients with modified Hoehn and Yahr (mH&Y) stage 1.5 walked with significantly faster gait cycle duration (faster rhythm) than those with stages 2.5–3.0 and normal control subjects, although there was no significant difference in gait cycle duration between patients with stages 2.5–3.0 PD and normal control subjects. The lower gait accelerations (i.e., inappropriate force production) can be the cause of narrow stride [42, 43]. Thus, the above results suggest that PD patients cannot walk with a sufficient stride throughout the clinical course, which can be compensated by increasing stepping [48, 55–57] at the early stage, but not at the advanced stage [48]. Our data suggest that computation of acceleration of gait movements and gait cycle duration from daily walking can be useful in the assessment of the clinical stage of the gait disorder.

#### **4.3. Narrow range of gait-induced acceleration and gait cycle duration: Possible index of monotony in PD**

patients with scores 1–15 (0.40 ± 0.09 m/sec2

(0.48 ± 0.09 m/sec2

(C), whereas it is flat in (D).

36 Wearable Technologies

) (*p* < 0.05) and that of patients with scores 16–30

) (*p* < 0.01). Furthermore, in 50 patients with untreated PD, the *amount of*

*overall movements per 24 hrs* was significantly associated with the UPDS part II score and part III score [53]. These results suggest that the *amount of overall movements per 24 hrs* can be a good index for poverty of movements and slowness/smallness of executed movements in ADL.

**Figure 4.** (A, B) Probability distribution of the averaged acceleration per 10 min, obtained from 24 hrs of continuous recording. (C, D) examples of gait acceleration-cycle duration relationship in a patient with Parkinson's disease (PD) based on 24 hrs of gait recording. Black thick line: Corresponding regression line. In (C) and (D), red thin line: Regression line obtained from the summed average of 17 normal control subjects. The regression line of the PD patient is steep in

The relationship between gait-induced acceleration and gait cycle duration (*gait accelerationcycle duration curves*) [45, 46] is shown in **Figure 4C** and **D**. Both parameters were recorded continuously for more than 24 hrs, and then the average value of every 10 min was computed

**4. Quantitative estimation of parkinsonian gait using two**

**parameters: "gait-induced acceleration" and "gait cycle duration"**

**4.1. Relationship between gait-induced acceleration and gait cycle duration**

Another characteristic change in the shape of the *gait acceleration-cycle duration curv*e is the narrowness of the range of force and/or rhythm [45, 46]. When PD patients walked with a narrow range of change in gait acceleration (i.e., floor reaction forces), the slope of the regression line was steep (*steep type*) (**Figure 4C**). In contrast, when PD patients walked with a narrow

is, PD patients cannot dynamically vary the force and rhythm in their daily walking, resulting in a narrow range of floor reaction forces and step cycles. Our data suggest that quantification of both the range of gait-induced acceleration and gait cycle duration provides a useful index

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily…

http://dx.doi.org/10.5772/intechopen.75483

39

Although dopamine replacement therapy is effective at the early stage, patients with PD develop motor fluctuation as the disease progresses [13, 47, 60–62]. By detecting changes in gait parameters, we tried to determine motor fluctuations. We review here the following two

We traced changes in gait-induced accelerations and cadences (steps/min, a parameter identical to gait cycle duration). These changes in gait force and rhythm parameters were compared simultaneously with patients' diaries. A total of 44 patients with PD, who showed simultaneous changes between accelerations and cadences, and 17 normal control subjects participated

No subjective fluctuations were noticed by 23 of these 44 patients. However, the PGR detected pathological shift of accelerations and cadences in 19 of the 23 patients (83%). These patients showed fast stepping or slow stepping with low amplitude of accelerations. On the other hand, the other 21 patients complained of subjective off in their diaries. Long-term tracing of gait-induced accelerations and cadences identified no changes in 11 of these 21 patients (52%). Surprisingly, no synchronization was noted between the subjective off and gait off determined by the PGR in 30 of the 44 patients (68%). This discrepancy could be in part due to deficits in attention to motor deficits [63]. Our data suggest that tracing simultaneous changes in acceleration and cadence can raise an interesting question: "How do PD patients subjec-

Clinically, improvements in gait fluctuations of PD patients after the addition or increase in dose of an anti-parkinsonism medicine were detected by examining changes in gait force and rhythm parameters [54]. The present method had a high sensitivity for detecting improve-

We reported previously [42, 43] that the relationship between gait-induced acceleration and gait cycle duration can be described by the following function derived from an inverted pen-

**5.2. Method II: quantitation of degree of motor fluctuation by calculating frequency**

**5.1. Method I: identification of "off" by tracing daily changes in gait-induced**

of monotony, a fundamental pathophysiology in PD.

**accelerations and gait cycle duration**

methods.

in this study [47].

tively notice the off time?"

ments than the UPDRS scores.

**mode change in gait**

*5.2.1. Two strategies*

dulum model:

**5. Quantitative estimation of motor fluctuation**

**Figure 5.** The mean gait acceleration (A) and mean gait cycle (B) in PD patients according to the modified Hoehn and Yahr stages. Differences between each stage group and normal control subjects, and among the stage groups were analyzed for statistical significance (\**p* < 0.05, \*\**p* < 0.01, by *t-*test) [40].

range of change in the gait cycle duration (i.e., rhythm), the regression line was flat (*flat type*) (**Figure 4D**). Other patients walked with a narrow range of both acceleration and cycle, and their acceleration and cycle duration data were scattered within a narrow range (*lump type*). Under these conditions, the patient always walks with the preferred floor reaction forces or cycle, that is, monotone walking.

For quantitative assessment of these changes, we defined *%alteration range* as an index of monotone walking [46]. The mean and standard deviation (SD) of gait acceleration and cycle duration were calculated from the *gait acceleration-step cycle duration curve*, which was constructed from a single long-term recording from each patient. Here, we used the SD as an index of alteration range. The interindividual mean of SD was calculated from data of 17 control subjects. Thus, the ratio of the SD of each patient to the interindividual mean of SD of the normal control subjects was defined as *%alteration range*. The cutoff level was set at 75% in analysis of the *%alteration range*.

Changes in *%alteration range* were examined in 40 patients with PD. In this analysis, the regression in 16 of the 40 (40%) patients was similar to that of the control (*%alteration range* > 75%). Furthermore, the *steep type* (*%alteration range* of gait acceleration of <75%) was identified in 12 of the 40 patients (30%), whereas the *flat type* (*%alteration range* of gait cycle of <75%) was noted in 8 of the 20 (20%) patients, and the *lump pattern* was noted in 10% of patients (4/40) [46].

It has been reported that the maximum activities that can be generated in a muscle burst is low in PD patients [58] and that PD patients can only execute movements of different amplitudes at a single, slow velocity without dynamic changes of the movement velocity [59]. These studies suggest a possible mechanism for the narrow range of force and rhythm in gait, that is, PD patients cannot dynamically vary the force and rhythm in their daily walking, resulting in a narrow range of floor reaction forces and step cycles. Our data suggest that quantification of both the range of gait-induced acceleration and gait cycle duration provides a useful index of monotony, a fundamental pathophysiology in PD.

### **5. Quantitative estimation of motor fluctuation**

Although dopamine replacement therapy is effective at the early stage, patients with PD develop motor fluctuation as the disease progresses [13, 47, 60–62]. By detecting changes in gait parameters, we tried to determine motor fluctuations. We review here the following two methods.

### **5.1. Method I: identification of "off" by tracing daily changes in gait-induced accelerations and gait cycle duration**

We traced changes in gait-induced accelerations and cadences (steps/min, a parameter identical to gait cycle duration). These changes in gait force and rhythm parameters were compared simultaneously with patients' diaries. A total of 44 patients with PD, who showed simultaneous changes between accelerations and cadences, and 17 normal control subjects participated in this study [47].

No subjective fluctuations were noticed by 23 of these 44 patients. However, the PGR detected pathological shift of accelerations and cadences in 19 of the 23 patients (83%). These patients showed fast stepping or slow stepping with low amplitude of accelerations. On the other hand, the other 21 patients complained of subjective off in their diaries. Long-term tracing of gait-induced accelerations and cadences identified no changes in 11 of these 21 patients (52%). Surprisingly, no synchronization was noted between the subjective off and gait off determined by the PGR in 30 of the 44 patients (68%). This discrepancy could be in part due to deficits in attention to motor deficits [63]. Our data suggest that tracing simultaneous changes in acceleration and cadence can raise an interesting question: "How do PD patients subjectively notice the off time?"

Clinically, improvements in gait fluctuations of PD patients after the addition or increase in dose of an anti-parkinsonism medicine were detected by examining changes in gait force and rhythm parameters [54]. The present method had a high sensitivity for detecting improvements than the UPDRS scores.

#### **5.2. Method II: quantitation of degree of motor fluctuation by calculating frequency mode change in gait**

#### *5.2.1. Two strategies*

range of change in the gait cycle duration (i.e., rhythm), the regression line was flat (*flat type*) (**Figure 4D**). Other patients walked with a narrow range of both acceleration and cycle, and their acceleration and cycle duration data were scattered within a narrow range (*lump type*). Under these conditions, the patient always walks with the preferred floor reaction forces or

**Figure 5.** The mean gait acceleration (A) and mean gait cycle (B) in PD patients according to the modified Hoehn and Yahr stages. Differences between each stage group and normal control subjects, and among the stage groups were

For quantitative assessment of these changes, we defined *%alteration range* as an index of monotone walking [46]. The mean and standard deviation (SD) of gait acceleration and cycle duration were calculated from the *gait acceleration-step cycle duration curve*, which was constructed from a single long-term recording from each patient. Here, we used the SD as an index of alteration range. The interindividual mean of SD was calculated from data of 17 control subjects. Thus, the ratio of the SD of each patient to the interindividual mean of SD of the normal control subjects was defined as *%alteration range*. The cutoff level was set at 75% in

Changes in *%alteration range* were examined in 40 patients with PD. In this analysis, the regression in 16 of the 40 (40%) patients was similar to that of the control (*%alteration range* > 75%). Furthermore, the *steep type* (*%alteration range* of gait acceleration of <75%) was identified in 12 of the 40 patients (30%), whereas the *flat type* (*%alteration range* of gait cycle of <75%) was noted in 8 of the 20 (20%) patients, and the *lump pattern* was noted in 10% of patients (4/40) [46].

It has been reported that the maximum activities that can be generated in a muscle burst is low in PD patients [58] and that PD patients can only execute movements of different amplitudes at a single, slow velocity without dynamic changes of the movement velocity [59]. These studies suggest a possible mechanism for the narrow range of force and rhythm in gait, that

cycle, that is, monotone walking.

38 Wearable Technologies

analyzed for statistical significance (\**p* < 0.05, \*\**p* < 0.01, by *t-*test) [40].

analysis of the *%alteration range*.

We reported previously [42, 43] that the relationship between gait-induced acceleration and gait cycle duration can be described by the following function derived from an inverted pendulum model:

$$a\_{\mathbf{v}} = \frac{1}{T\_c} \left[ \alpha \left( \frac{1}{T\_c} - \frac{1}{T\_o} \right) + \beta \right]^2, \quad \mathbf{v} \in \mathbb{R}^{N \times N}$$

where *I*(*t*) reflects the walking mode [i.e., active (0) or quiet (1)] in each non-overlapping 1 hr. interval during the day and *t* signifies the time index [*t* = 1, 2, …, *n* (*n* ≤ 24)]. FC was 0.22 ± 0.12 in normal subjects compared with 0.35 ± 0.13 in patients with mild PD (mH&Y stage <2.5) and 0.48 ± 0.25 in patients with severe PD (mH&Y stage ≥2.5) (*p* < 0.05) (**Figure 6B**) [49]. In PD, the quiet mode coincides approximately with slow stepping gait mode. These results show that estimation of frequency of change between quiet and active modes provides an

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily…

http://dx.doi.org/10.5772/intechopen.75483

41

Our newly developed wearable device and algorithm, the PGR, allows continuous monitoring of gait for up to 70 hrs and can differentiate precisely gait-induced accelerations from other movements. The PGR can monitor both the overall motor activities and gait movements in daily activities. The findings obtained from long-term monitoring using PGR are classified

Acceleration values induced by various movements, averaged every 10 min, showed gamma distribution. The mean value in this distribution was used as an index of the *amount of overall movements per 24 hrs*. The *amount of overall movements per 24 hrs* was lower in PD patients, especially in patients with UPDRS-III ≥ 30, suggesting that this parameter reflects the range from

Analysis of *the gait acceleration-cycle duration curve* provided the mean values and ranges of gaitinduced acceleration and gait cycle duration. Quantitative analysis showed force generation deficits, disabilities of gait rhythm setting, and loss of dynamic modulation of the force and rhythm in each patient with PD. The quantitative measures can be used for assessment of the degree of parkinsonian gait disorders. These changes in gait-induced acceleration reflect PD-specific deficits in neural circuits involved in gait control [1, 2, 10]. Thus, it is hoped that long-term monitoring of these parameters can identify the pathophysiological changes in gait disorders in PD.

Furthermore, the device also provides assessment of motor fluctuation from the data of gait fluctuation in any particular day. By tracing changes in gait-induced accelerations and gait cycle durations, we identified the time of motor off, which was not necessarily noticed by patients. Measurement of the frequency of changes in quiet mode (i.e., slow steeping mode)

Taken together, measurement of overall movement- and gait-induced accelerations using a wearable device could be useful for clinical assessment of parkinsonian status. Especially, the

and active mode provides information on the degree of severity of motor fluctuation.

**6.4. Open questions and possible clinical applications**

**6.1. Poverty of movements and slowness/smallness of executed movements**

poverty of movements to slowness/smallness of executed movements.

estimate of the degree of motor fluctuation.

**6. Conclusion**

into three categories.

**6.2. Gait disorders**

**6.3. Motor fluctuation**

where *T*c is the gait cycle, *a*v. is the vertical acceleration magnitude averaged over *T*c, and *α*, *β*, and *T*0 are the three separate parameters. Parameters *α* and *β* are subject-specific, representing a particular walking strategy of an individual, *α* quantifies how to regulate gait acceleration according to different gait paces, and *β* is something like a basic potential force that drives natural walking. In contrast, *T*0 is assumed to be a constant across subjects. Specifically, we set *T*0 = 1.4 sec in order to uncorrelate *α* and *β* with each other [42, 43]. The above mathematical method was used to build better fitting regression curves from the data. In other words, this mathematical method for regression curves represents improvement compared to that shown in **Figure 4C** and **D**.

**Figure 6A** shows that two regression lines are necessary for better fitting of plots obtained from long-term daily walking, suggesting that a subject usually walks with two types of gait mode [49]. The steep regression line with small accelerations for a particular cycle means quiet walking, for example, walking within the house, whereas the flat regression line with large accelerations for a particular cycle reflects active walking, for example, walking in the street. These results suggest that the subject changes the walking strategy during daily walking between "quiet mode" and "active mode." To identify the involvement of deficits in higher functions, we quantitatively examined the "switching" from one mode to another [49].

#### *5.2.2. Frequency of mode change*

A total of 26 patients with PD and 13 normal control subjects participated in this study [49]. We introduced the parameter of *frequency of mode chan*ge (FC), which describes the frequency of changes in walking strategies and is calculated mathematically by the following equation:

$$\text{FC} = \frac{\sum\_{t=1}^{n-1} |I(t+1) - I(t)|}{n-1}$$

**Figure 6.** (A) Examples of gait acceleration-cycle duration relationship in a normal subject based on 24 hrs of gait recording. The amplitude of gait-related accelerations and duration of gait cycles were plotted for all stride events. Two regression lines were required for better fitting. (B) Frequency of mode change (FC) that describes the frequency of changes into different walking strategies between quiet mode and active mode.

where *I*(*t*) reflects the walking mode [i.e., active (0) or quiet (1)] in each non-overlapping 1 hr. interval during the day and *t* signifies the time index [*t* = 1, 2, …, *n* (*n* ≤ 24)]. FC was 0.22 ± 0.12 in normal subjects compared with 0.35 ± 0.13 in patients with mild PD (mH&Y stage <2.5) and 0.48 ± 0.25 in patients with severe PD (mH&Y stage ≥2.5) (*p* < 0.05) (**Figure 6B**) [49]. In PD, the quiet mode coincides approximately with slow stepping gait mode. These results show that estimation of frequency of change between quiet and active modes provides an estimate of the degree of motor fluctuation.

### **6. Conclusion**

*a*v = \_\_1

natural walking. In contrast, *T*0

*5.2.2. Frequency of mode change*

FC =

in **Figure 4C** and **D**.

40 Wearable Technologies

*T*c [ *α*( \_\_1 *T*c − \_\_1 *T*0) + *β* ] 2 ,

where *T*c is the gait cycle, *a*v. is the vertical acceleration magnitude averaged over *T*c, and *α*, *β*, and *T*0 are the three separate parameters. Parameters *α* and *β* are subject-specific, representing a particular walking strategy of an individual, *α* quantifies how to regulate gait acceleration according to different gait paces, and *β* is something like a basic potential force that drives

*T*0 = 1.4 sec in order to uncorrelate *α* and *β* with each other [42, 43]. The above mathematical method was used to build better fitting regression curves from the data. In other words, this mathematical method for regression curves represents improvement compared to that shown

**Figure 6A** shows that two regression lines are necessary for better fitting of plots obtained from long-term daily walking, suggesting that a subject usually walks with two types of gait mode [49]. The steep regression line with small accelerations for a particular cycle means quiet walking, for example, walking within the house, whereas the flat regression line with large accelerations for a particular cycle reflects active walking, for example, walking in the street. These results suggest that the subject changes the walking strategy during daily walking between "quiet mode" and "active mode." To identify the involvement of deficits in higher functions, we quantitatively examined the "switching" from one mode to another [49].

A total of 26 patients with PD and 13 normal control subjects participated in this study [49]. We introduced the parameter of *frequency of mode chan*ge (FC), which describes the frequency of changes in walking strategies and is calculated mathematically by the following equation:

**Figure 6.** (A) Examples of gait acceleration-cycle duration relationship in a normal subject based on 24 hrs of gait recording. The amplitude of gait-related accelerations and duration of gait cycles were plotted for all stride events. Two regression lines were required for better fitting. (B) Frequency of mode change (FC) that describes the frequency of

changes into different walking strategies between quiet mode and active mode.

∑ *t*=1 *n*−1

is assumed to be a constant across subjects. Specifically, we set

Our newly developed wearable device and algorithm, the PGR, allows continuous monitoring of gait for up to 70 hrs and can differentiate precisely gait-induced accelerations from other movements. The PGR can monitor both the overall motor activities and gait movements in daily activities. The findings obtained from long-term monitoring using PGR are classified into three categories.

#### **6.1. Poverty of movements and slowness/smallness of executed movements**

Acceleration values induced by various movements, averaged every 10 min, showed gamma distribution. The mean value in this distribution was used as an index of the *amount of overall movements per 24 hrs*. The *amount of overall movements per 24 hrs* was lower in PD patients, especially in patients with UPDRS-III ≥ 30, suggesting that this parameter reflects the range from poverty of movements to slowness/smallness of executed movements.

### **6.2. Gait disorders**

Analysis of *the gait acceleration-cycle duration curve* provided the mean values and ranges of gaitinduced acceleration and gait cycle duration. Quantitative analysis showed force generation deficits, disabilities of gait rhythm setting, and loss of dynamic modulation of the force and rhythm in each patient with PD. The quantitative measures can be used for assessment of the degree of parkinsonian gait disorders. These changes in gait-induced acceleration reflect PD-specific deficits in neural circuits involved in gait control [1, 2, 10]. Thus, it is hoped that long-term monitoring of these parameters can identify the pathophysiological changes in gait disorders in PD.

#### **6.3. Motor fluctuation**

Furthermore, the device also provides assessment of motor fluctuation from the data of gait fluctuation in any particular day. By tracing changes in gait-induced accelerations and gait cycle durations, we identified the time of motor off, which was not necessarily noticed by patients. Measurement of the frequency of changes in quiet mode (i.e., slow steeping mode) and active mode provides information on the degree of severity of motor fluctuation.

### **6.4. Open questions and possible clinical applications**

Taken together, measurement of overall movement- and gait-induced accelerations using a wearable device could be useful for clinical assessment of parkinsonian status. Especially, the Quantification of "poverty of movements" and "slowness/ smallness of executed movements":

• Acceleration values induced by various movements are averaged every 10 min from the long-term monitoring data

**Author details**

Masahiko Suzuki1

Satoshi Orimo9

Mitsuru Yoneyama4

Medicine, Tokyo, Japan

Welfare, Tochigi, Japan

**References**

\*, Makiko Yogo1

 and Hiroshi Mitoma10 \*Address all correspondence to<EMAIL_ADDRESS>
5 LSI Medience Corporation, Tokyo, Japan

, Masato Takada5

2 Department of Neurology, Tokyo Medical University, Tokyo, Japan

9 Department of Neurology, Kanto Chuo Hospital, Tokyo, Japan

European Journal of Neurology. 2003 Jul;**10**(4):391-398

[4] Critchley M. Arteriosclerotic parkinsonism. Brain. 1929;**52**:23-83

particularly in the elderly. Neurology. 1993 Feb;**43**(2):268-279

thy: Binswanger's disease. Movement Disorders. 1987;**2**(1):1-8

3 Department of Neurology, Tokyo Women's Medical University, Tokyo, Japan 4 Mitsubishi Chemical Corporation, Yokohama R&D Center, Yokohama, Japan

6 Department of Neurology, Shioya Hospital, International University of Health and

7 Department of Neurology, Shizuoka Hospital, Juntendo University, Shizuoka, Japan 8 Department of Rehabilitation, Urayasu Hospital, Juntendo University, Chiba, Japan

10 Medical Education Promotion Center, Tokyo Medical University, Tokyo, Japan

[1] Dietz V. Human neuronal control of automatic functional movements: Interaction between central programs and afferent input. Physiological Reviews. 1992 Jan;**72**(1):33-69

[2] Dietz V. Neurophysiology of gait disorders: Present and future application. Electro-

[3] Schaafsma JD, Balash Y, Gurevich T, Bartels AL, Hausdorff JM, Giladi N. Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson's disease.

[5] Nutt JG, Marsden CD, Thompson PD. Human walking and higher-level gait disorders,

[6] Thompson PD, Marsden CD. Gait disorder of subcortical arteriosclerotic encephalopa-

encephalography and Clinical Neurophysiology. 1997 Sep;**103**(3):333-355

, Masayo Morita1

, Hiroya Utsumi6

1 Department of Neurology, Katsushika Medical Center, The Jikei University School of

, Hiroo Terashi2

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily…

, Yasuyuki Okuma7

, Mutsumi Iijima3

http://dx.doi.org/10.5772/intechopen.75483

,

,

43

, Akito Hayashi8

• The mean value in this gamma distribution can be an index of the *amount of overall movements per 24 hrs*

Gait disorders:

Motor fluctuation:

**Table 3.** Protocol for quantitative assessment of poverty of movements and slowness/smallness of executed movements, gait disorders, and motor fluctuation.

new algorithm that defines the relationship between gait-induced acceleration and gait cycle duration could be useful in clinical applications. However, there are still the following open questions:

Here, we provisionally propose a comprehensive and quantitative examination protocol for poverty of movements and slowness/smallness of executed movements, gait disorders, and motor fluctuation in patients with PD using an accelerator-mounted wearable device (see **Table 3**). This physiology-based protocol would make it possible to quantify the extent of parkinsonian motor signs and/or the effect of drugs. We anticipate that further analysis of daily walking using the PGR would contribute to the development of better therapies for PD.

#### **Conflict of interest**

The authors declare no conflict of interest.

## **Author details**

Masahiko Suzuki1 \*, Makiko Yogo1 , Masayo Morita1 , Hiroo Terashi2 , Mutsumi Iijima3 , Mitsuru Yoneyama4 , Masato Takada5 , Hiroya Utsumi6 , Yasuyuki Okuma7 , Akito Hayashi8 , Satoshi Orimo9 and Hiroshi Mitoma10

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Neurology, Katsushika Medical Center, The Jikei University School of Medicine, Tokyo, Japan

2 Department of Neurology, Tokyo Medical University, Tokyo, Japan

3 Department of Neurology, Tokyo Women's Medical University, Tokyo, Japan

4 Mitsubishi Chemical Corporation, Yokohama R&D Center, Yokohama, Japan

5 LSI Medience Corporation, Tokyo, Japan

6 Department of Neurology, Shioya Hospital, International University of Health and Welfare, Tochigi, Japan

7 Department of Neurology, Shizuoka Hospital, Juntendo University, Shizuoka, Japan

8 Department of Rehabilitation, Urayasu Hospital, Juntendo University, Chiba, Japan

9 Department of Neurology, Kanto Chuo Hospital, Tokyo, Japan

10 Medical Education Promotion Center, Tokyo Medical University, Tokyo, Japan

### **References**

new algorithm that defines the relationship between gait-induced acceleration and gait cycle duration could be useful in clinical applications. However, there are still the following open

**Table 3.** Protocol for quantitative assessment of poverty of movements and slowness/smallness of executed movements,

• Our studies were conducted in a small number of normal subjects and patients with

• Methodologically, our analysis does not include quantification of stride length. For ex-

Here, we provisionally propose a comprehensive and quantitative examination protocol for poverty of movements and slowness/smallness of executed movements, gait disorders, and motor fluctuation in patients with PD using an accelerator-mounted wearable device (see **Table 3**). This physiology-based protocol would make it possible to quantify the extent of parkinsonian motor signs and/or the effect of drugs. We anticipate that further analysis of daily walking using the PGR would contribute to the development of

ample, analysis using an inverted pendulum model should be useful [64].

PD. Thus, the outcome from our data has not been validated.

Quantification of "poverty of movements" and "slowness/ smallness of executed movements":

• Gait-induced accelerations are averaged every 10 min from the long-term monitoring data

• The *gait acceleration-cycle duration curve* is calculated from these plots

rhythm setting mechanisms, respectively

• Acceleration values induced by various movements are averaged every 10 min from the long-term monitoring

• The mean value in this gamma distribution can be an index of the *amount of overall movements per 24 hrs*

• Changes in the mean values of acceleration and cycle duration can reflect deficits in force production and

• The time of motor off can be determined by tracing changes in gait-induced accelerations and gait cycle

• Measurement of frequency of changes between quiet and active modes provides an index of the severity of

• Narrowness of accelerations and cycle duration can reflect loss of dynamic modulation of the force and rhythm

questions:

data

42 Wearable Technologies

Gait disorders:

control Motor fluctuation:

durations

motor fluctuation

gait disorders, and motor fluctuation.

better therapies for PD.

**Conflict of interest**

The authors declare no conflict of interest.

[7] Thompson PD. Gait Disorders Accompanying Diseases of the Frontal Lobes. In: Růžička E, Hallet M, Jankovic J, editors. Advances in Neurology. Gait Disorders. Philadelphia, USA: Lippincott Williams & Wilkins; 2001. pp. 235-241

[20] de Groot S, Nieuwenhuizen MG. Validity and reliability of measuring activities, movement intensity and energy expenditure with the DynaPort MoveMonitor. Medical

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45

[21] Mancini M, Horak FB. Potential of APDM mobility lab for the monitoring of the progression of Parkinson's disease. Expert Review of Medical Devices. 2016;**13**(5):452-466 [22] Dewey DC, Miocinovic S, Bernstein I, Khemani P, Dewey RB III, Querry R, Chitnis S, Dewey RB Jr. Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease. Journal of the Neurological Sciences. 2014;**345**(1-2):131-138 [23] Mariani B, Jiménez MC, Vingerhoets FJG, Aminian K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease. IEEE Transactions on

[24] Fisher JM, Hammerla NY, Ploetz T, Andras P, Rochester L, Walker RW. Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers. Parkinsonism and related disorders. 2016. DOI: doi.org/10.1016/j.park

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**Chapter 3**

Provisional chapter

**Wearable Technology as a Tool to Motivate Health**

DOI: 10.5772/intechopen.77002

According to the Scientific Committee on Occupational Exposure Limits, work-related exposures are estimated to account for about 15% of all adult respiratory diseases. Today, the use of personal protective equipment (PPE) is the only way for workers to prevent disease. Nevertheless, its use is highly sparse. Currently, products and systems embedded with wearable technologies are able to protect, motivate and educate users. The authors then suggested the development of a novel wearable system following the beliefs that wearable technology can be persuasive and elicit a conscious behaviour towards the use of the PPEs by consequently improving their health condition. The authors here describe the result of a Transnational Research Project named "P\_O\_D Plurisensorial Device to prevent Occupational Disease." The chapter describes the findings achieved so far, the research phase and the new wearable system conceived as a possible example of how to

Keywords: wearable technology, persuasive technologies, behaviour change, wearability,

The World Health Organization and Europe Mortality database in 2011 in Europe, estimated that 7200 cases of respiratory diseases are related to occupational exposures to Volatile Organic Compound (VOC) and dust. The Annual Inail (Istituto Nazionale Assicurazione Infortuni sul Lavoro) Report from 2015 confirms the European trend, putting in Italy the respiratory diseases at the third place (13.5%) between the occupational ones, also stressing the severity of their consequences. Volatile organic compounds (VOC) are—within working environment one of the highest causes of asthma, lung cancer, chronic obstructive pulmonary disease

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

use wearable technology as a useful tool to influence behavioural change.

Wearable Technology as a Tool to Motivate Health

**Behaviour: A Case Study**

Behaviour: A Case Study

http://dx.doi.org/10.5772/intechopen.77002

health, human-centred approach

Abstract

1. Introduction

Venere Ferraro, Mila Stepanovic and Silvia Ferraris

Venere Ferraro, Mila Stepanovic and Silvia Ferraris

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Wearable Technology as a Tool to Motivate Health Behaviour: A Case Study** Wearable Technology as a Tool to Motivate Health Behaviour: A Case Study

DOI: 10.5772/intechopen.77002

Venere Ferraro, Mila Stepanovic and Silvia Ferraris Venere Ferraro, Mila Stepanovic and Silvia Ferraris

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.77002

#### Abstract

[58] Hallett M, Khoshbin S.A physiological mechanism of bradykinesia. Brain. 1980 Jun;**103**(2):

[59] Flowers KA. Visual "closed-loop" and "open-loop" characteristics of voluntary movement in patients with parkinsonism and intention tremor. Brain. 1976 Jun;**99**(2):269-310

[60] Marsden CD, Parkes JD, Quinn N. Fluctuations of disability in Parkinson's disease: Clinical Aspects. In: Marsden CD, Fahn S, editors. Movement Disorders. London, UK:

[61] Giladi N. Medical treatment of freezing of gait. Movement Disorders. 2008;**23**:S482-S488 [62] Stacy M, Bowron A, Guttman M, Hauser R, Hughes K, Larsen JP, LeWitt P, Oertel W, Quinn N, Sethi K, Stocchi F. Identification of motor and nonmotor wearing-off in Parkinson's disease: Comparison of a patient questionnaire versus a clinician assess-

[63] Stacy M, Hauser R. Development of a patient questionnaire to facilitate recognition of motor and non-motor wearing-off in Parkinson's disease. Journal of Neural

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tions during human walking. Gait & Posture. 2003 Oct;**18**(2):1-10

301-314

48 Wearable Technologies

Butterworth Scientific; 1982. pp. 96-112

Transmission. 2007 Feb;**114**(2):211-217

ment. Movement Disorders. 2005 Jun;**20**(6):726-723

According to the Scientific Committee on Occupational Exposure Limits, work-related exposures are estimated to account for about 15% of all adult respiratory diseases. Today, the use of personal protective equipment (PPE) is the only way for workers to prevent disease. Nevertheless, its use is highly sparse. Currently, products and systems embedded with wearable technologies are able to protect, motivate and educate users. The authors then suggested the development of a novel wearable system following the beliefs that wearable technology can be persuasive and elicit a conscious behaviour towards the use of the PPEs by consequently improving their health condition. The authors here describe the result of a Transnational Research Project named "P\_O\_D Plurisensorial Device to prevent Occupational Disease." The chapter describes the findings achieved so far, the research phase and the new wearable system conceived as a possible example of how to use wearable technology as a useful tool to influence behavioural change.

Keywords: wearable technology, persuasive technologies, behaviour change, wearability, health, human-centred approach

#### 1. Introduction

The World Health Organization and Europe Mortality database in 2011 in Europe, estimated that 7200 cases of respiratory diseases are related to occupational exposures to Volatile Organic Compound (VOC) and dust. The Annual Inail (Istituto Nazionale Assicurazione Infortuni sul Lavoro) Report from 2015 confirms the European trend, putting in Italy the respiratory diseases at the third place (13.5%) between the occupational ones, also stressing the severity of their consequences. Volatile organic compounds (VOC) are—within working environment one of the highest causes of asthma, lung cancer, chronic obstructive pulmonary disease

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

(COPD) and respiratory tract infections. Moreover, the occupational disease percentage increment, in particular respiratory related diseases, is typically present in sectors such as agriculture, transportation and manufacturing which is considered also as the most dangerous with 5.172 cases of respiratory diseases in 2014 (INAIL). This text is funded on results gained from Transnational Research Project (SAF€RA Joint Call 2014) called "POD-Plurisensorial Device to Prevent Occupational Disease" which was developed by a consortium made up of Department of Design of Politecnico di Milano, Department of Design Engineering from Delft University of Technology and the Italian company Comftech. This project took place from July 2015 to August 2017. The data about the respiratory diseases shows the importance of wearing personal protective equipment (PPEs) preventing the users from the inhalation of dangerous particles. Starting from those data, the research project targets the development of a protective wearable system concept that is able to prevent respiratory diseases inside the environment of a coating plant.

them in design process. Engagement of the user in design process was executed in repetitive cycles divided in five design phases: empathy, define, ideate, prototype and test [7, 8]. In this chapter, the authors describe the entire design process that led to the generation of a new novel wearable system by involving and emphasising with the user in every step to define his/her expectations. This is in order to translate those expectations in a way where wearable technology is shaped purposefully for the final user by becoming a powerful tool to change their

Wearable Technology as a Tool to Motivate Health Behaviour: A Case Study

http://dx.doi.org/10.5772/intechopen.77002

51

Nowadays technology is taking over in different disciplines and with a different purpose: education, medicine and in general wellbeing, working environment, smart home, and so on. (Smartphone, smart watches, smart meter, run trackers, etc.). New Technologies have become deeply embedded in our ordinary everyday experiences by improving the way products and systems help, inform, engage and/or entertain us, coding new languages of communication and interaction. Different technologies change the way the people behave (interact) and also define the behaviour of artefacts, environment and system [9]. Technological products are able to motivate users and change their attitudes and behaviour but also to act beyond human capabilities. Donald Schön [10] defined "extension of human capabilities" as a main goal of

On the other hand, as Fogg stressed, technology can be persuasive and change user behaviours. "Persuasion is a non-coercive attempt to change attitudes or behaviours" [11]. Interactive technologies have been—and will continue to be—created to influence (persuade or motivate) people in different fields of application. Interactive products able to persuade and motivate users are evolving in parallel with a technological development and level of adeptness of users to different forms of interaction with advanced products, systems and environments. Recently, persuasive technologies are developing also with a purpose of improving health and wellbeing, among those commercial based on computers and Web applications as

Advanced technologies and solutions and information communication technology (ICT) have potential to be a tool for promoting positive health and lifestyle habits [12–16], giving social support [17, 18] and helping with lifestyle change [19, 20]. Furthermore, research on persuasive technology [21] and affective computing [22] is providing technological (partial) solutions for the development of systems and devices able to cope with health issues and awareness. There are many factors to consider when designing products or systems for persuasion. Some people are able to act and be motivated by themselves, while others need to be involved and motivated by others. Motivation is a key element in any system designed to assist users in changing

These kinds of technologies are supposed to persuade user in a logical and reasonable way. If designed well, digital persuasion has way more advantages with respect to the traditional one based on human-human persuasion. Fogg [2] underlines a few advantages of digital persuasion

a principal tool of interaction (i.e. selling products or services).

habits in the use of the PPEs.

technology.

their health behaviour [23].

2. Wearable technology to persuade

The best method to reduce work-related diseases and improve general health of workers is prevention (International Labour office). Prevention can be done on two levels, which are (1) environmental monitoring and (2) the use of Personal Protective Equipment (PPE) which is also mandatory but often neglected by workers because they do not see the benefits of wearing it. A way to encourage the use of the PPEs could be to make workers aware of work-related risks they undertake and their health status by exploiting persuasive technologies in the shape of wearable devices.

Nowadays, the emerging smart technologies are able to support humans and can be persuasive and change humans' habits and behaviour without coercion. Intelligent technological products are able to change users' behaviour and educate, rather than only inform or do something for humans [1]. Technology might resolve the problem of subjectivity and precision by collecting and providing objective data that are based on real values of monitored subjects, objects and environments.  Persuasive technologies are defined as "computerbased tools designed for the purpose of changing people's attitudes and behaviours" [2].

The authors investigated in particular the use of smart technology that belongs to the class of wearable technology. Wearable sensors and systems are defined as wearable sensors/actuators and sensor-based communicative systems that can monitor and/or stimulate, treat and replace biophysical human functions. Wearable technology's purpose is to facilitate everyday life and also protect and inform users in order to avoid human errors, as those related to subjectivity of senses and perception [3–5].

Development of this research project consisted also in investigating technological solutions from other fields of study that have a similar purpose, as those in particular: medical (i.e. wearable monitoring systems for physiological parameters) and military solutions (i.e. the electronic nose developed by NASA). The main aim of the project was to create a meaningful interaction with the device able to motivate users to wear PPE by enhancing their consciousness related to the risks in the working place. Here the technology is supposed to make risks perceivable to users by obtaining valuable interaction [6]. The entire research was based on human-centred approach that relates wishes and needs of users, considering engagement of them in design process. Engagement of the user in design process was executed in repetitive cycles divided in five design phases: empathy, define, ideate, prototype and test [7, 8]. In this chapter, the authors describe the entire design process that led to the generation of a new novel wearable system by involving and emphasising with the user in every step to define his/her expectations. This is in order to translate those expectations in a way where wearable technology is shaped purposefully for the final user by becoming a powerful tool to change their habits in the use of the PPEs.

### 2. Wearable technology to persuade

(COPD) and respiratory tract infections. Moreover, the occupational disease percentage increment, in particular respiratory related diseases, is typically present in sectors such as agriculture, transportation and manufacturing which is considered also as the most dangerous with 5.172 cases of respiratory diseases in 2014 (INAIL). This text is funded on results gained from Transnational Research Project (SAF€RA Joint Call 2014) called "POD-Plurisensorial Device to Prevent Occupational Disease" which was developed by a consortium made up of Department of Design of Politecnico di Milano, Department of Design Engineering from Delft University of Technology and the Italian company Comftech. This project took place from July 2015 to August 2017. The data about the respiratory diseases shows the importance of wearing personal protective equipment (PPEs) preventing the users from the inhalation of dangerous particles. Starting from those data, the research project targets the development of a protective wearable system concept that is able to prevent respiratory diseases inside the environment of

The best method to reduce work-related diseases and improve general health of workers is prevention (International Labour office). Prevention can be done on two levels, which are (1) environmental monitoring and (2) the use of Personal Protective Equipment (PPE) which is also mandatory but often neglected by workers because they do not see the benefits of wearing it. A way to encourage the use of the PPEs could be to make workers aware of work-related risks they undertake and their health status by exploiting persuasive technologies in the shape

Nowadays, the emerging smart technologies are able to support humans and can be persuasive and change humans' habits and behaviour without coercion. Intelligent technological products are able to change users' behaviour and educate, rather than only inform or do something for humans [1]. Technology might resolve the problem of subjectivity and precision by collecting and providing objective data that are based on real values of monitored subjects, objects and environments.  Persuasive technologies are defined as "computerbased tools designed for the purpose of changing people's attitudes and behaviours" [2].

The authors investigated in particular the use of smart technology that belongs to the class of wearable technology. Wearable sensors and systems are defined as wearable sensors/actuators and sensor-based communicative systems that can monitor and/or stimulate, treat and replace biophysical human functions. Wearable technology's purpose is to facilitate everyday life and also protect and inform users in order to avoid human errors, as those related to subjectivity of

Development of this research project consisted also in investigating technological solutions from other fields of study that have a similar purpose, as those in particular: medical (i.e. wearable monitoring systems for physiological parameters) and military solutions (i.e. the electronic nose developed by NASA). The main aim of the project was to create a meaningful interaction with the device able to motivate users to wear PPE by enhancing their consciousness related to the risks in the working place. Here the technology is supposed to make risks perceivable to users by obtaining valuable interaction [6]. The entire research was based on human-centred approach that relates wishes and needs of users, considering engagement of

a coating plant.

50 Wearable Technologies

of wearable devices.

senses and perception [3–5].

Nowadays technology is taking over in different disciplines and with a different purpose: education, medicine and in general wellbeing, working environment, smart home, and so on. (Smartphone, smart watches, smart meter, run trackers, etc.). New Technologies have become deeply embedded in our ordinary everyday experiences by improving the way products and systems help, inform, engage and/or entertain us, coding new languages of communication and interaction. Different technologies change the way the people behave (interact) and also define the behaviour of artefacts, environment and system [9]. Technological products are able to motivate users and change their attitudes and behaviour but also to act beyond human capabilities. Donald Schön [10] defined "extension of human capabilities" as a main goal of technology.

On the other hand, as Fogg stressed, technology can be persuasive and change user behaviours. "Persuasion is a non-coercive attempt to change attitudes or behaviours" [11]. Interactive technologies have been—and will continue to be—created to influence (persuade or motivate) people in different fields of application. Interactive products able to persuade and motivate users are evolving in parallel with a technological development and level of adeptness of users to different forms of interaction with advanced products, systems and environments. Recently, persuasive technologies are developing also with a purpose of improving health and wellbeing, among those commercial based on computers and Web applications as a principal tool of interaction (i.e. selling products or services).

Advanced technologies and solutions and information communication technology (ICT) have potential to be a tool for promoting positive health and lifestyle habits [12–16], giving social support [17, 18] and helping with lifestyle change [19, 20]. Furthermore, research on persuasive technology [21] and affective computing [22] is providing technological (partial) solutions for the development of systems and devices able to cope with health issues and awareness. There are many factors to consider when designing products or systems for persuasion. Some people are able to act and be motivated by themselves, while others need to be involved and motivated by others. Motivation is a key element in any system designed to assist users in changing their health behaviour [23].

These kinds of technologies are supposed to persuade user in a logical and reasonable way. If designed well, digital persuasion has way more advantages with respect to the traditional one based on human-human persuasion. Fogg [2] underlines a few advantages of digital persuasion compared to traditional ones and those are related to the fact that the "computers" are much more interesting than people in the long-term persuasion process, they offer more anonymity, and they arrive at the contexts where human beings might not arrive.

Value of wearables is not in having a device that is carried on or held but in its possibility to be worn and integrated in everyday life and activities. A wearable device is not supposed to create the barrier between the wearer and outer world; thus, it needs to have a certain aesthetical quality and be approached as a part of clothing in order to achieve requirements of wearing it everywhere, by everyone and anytime [27]. Despite the fact that commercial wearable devices have recently become more common, up till now there are only few products

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53

User doesn't really understand the advantages of wearing such devices because often these devices are perceived as uncomfortable and alien to users. This occurs especially when the requirements referring to the formal and ergonomic characteristics are not well accomplished well as adaptability and adhesion to the body and dimensions of the housing [28]. There are different theoretical definitions about comfort, but all of them embrace the concept of creating harmony between the individual and environment and avoiding unpleasantness on all levels of existence. Any good design should consider both a user's need—as in this case is the comfort and body anatomic—and also the aspiration—which would be the designer's knowhow. In this way it is possible to design devices that meet both perceptual needs and needs

The word "wearability" literally means ability to wear and concerns the physical shape of wearables and their active relationship with the human form. The target is to define the correct/pleasant interaction between the human body and the wearable object by trying to

Applying human-centred approach in designing means digging in worker's behaviour, perception, working environment and generally social aspects of the work. This does not exclude identifying of product's technical requirements and issues related to wearable technologies. Merging these two in designing process makes possible the achievement of an appropriate design solution both from the point of technological adeptness and general comfort related to

In the development of the new wearable system, authors had to take into account the product language and the correct pleasant interaction between the product and the user but also to design for motivation. Indeed, authors found important emphasising the factor of influencing more responsible users' behaviour towards their health conditions through the method of personal and environmental monitoring. Introducing the technology and technological solutions in this context is a way to provide objective data to the user and raise awareness and motivation. When designing devices for persuasion, there is also a high demand on the way the feedback is transmitted to the user. For example, the reason why health awareness is accomplished with this kind of devices or applications is because it is in the real time—the gap between action and

For some applications, there is the necessity to create a real-time feedback. If we need a device to persuade on longer periods, feedback out of the context is a valid consideration. One of the often-used methods for creating persuasive technologies is personalisation. Personalised products are more likely to be accepted by the users. Designing personalised product does not have to be realistically addressed to a single person, but that it gives a feeling of being personal [2].

figure out a flexible shape without interfering with human daily activity [30].

from this category that satisfy all requirements related to users' needs.

related to wearability of the user [29].

information (feedback) is denied [24].

wearability [31].

Among emerging—persuasive—technologies, the wearable ones have the potential to build motivation by increasing health awareness through timely feedback [24]. A multidisciplinary approach in developing wearable technology based on biology, physiology, nanotechnology, material science and other numerous disciplines is influencing rapid increase both in product and in research development. These devices are often based on sensing technology, communication and persuasion [25].

So far, in the field of health, the wearable technologies have predominately focused on diet and physical activity, such as weight loss, motivating physical activity, maintaining exercise routines and an overall better understanding of an individual's health. In the commercial sector, one of the earlier more successful devices includes Nike+iPod (nikeplus.com), which employs a sensor in the runner's shoe and an iPod to keep track of the current pace and distance of a workout. In addition to tracking personal workout statistics, the runner can connect to an online community through the Nike+ website where he/she can track goals, compare running times with others and challenge friends. While Nike+ is designed for the specific activity of running, FitBit (fitbit.com), BodyMedia Fit (body-media.com) and Jawbone UP (jawbone.com) track everyday activities like walking (steps taken), climbing stairs (number of floors) and sleeping (hours and quality of sleep). Much like Nike+, all these wearable devices sync with an e-health application through a wireless base station. Additionally, they provide users with the ability to log individual foods and workouts from a smart phone or computer to track diet and exercise. BodyMedia Fit is geared towards weight loss and is worn as an armband, while FitBit and UP focus on smaller lifestyle changes and are clipped to the waist and worn on the wrist, respectively.

Beside previous examples, wearable systems can be designed with a purpose to provide more control to the users and focus on more health consciousness increasing applications rather than just monitoring because presumably the prevention and health sector is one of the strongest areas of innovation [26].

There are activities and environments for which are required external incentives and motivations. As regards, the objective of our research project was in designing a protective and preventive wearable system that consists basically in monitoring of the user's physiological parameters (1), environmental monitoring and hazard detecting (2), real-time feedback providing (3) and allowing data transmission.

### 3. Method

Since when designing wearable technologies it is very important to focus on user's needs, development of research project was funded on human-centred approach, aimed at meeting not only technological requirements but also and especially user's needs and meaningful experience.

Value of wearables is not in having a device that is carried on or held but in its possibility to be worn and integrated in everyday life and activities. A wearable device is not supposed to create the barrier between the wearer and outer world; thus, it needs to have a certain aesthetical quality and be approached as a part of clothing in order to achieve requirements of wearing it everywhere, by everyone and anytime [27]. Despite the fact that commercial wearable devices have recently become more common, up till now there are only few products from this category that satisfy all requirements related to users' needs.

compared to traditional ones and those are related to the fact that the "computers" are much more interesting than people in the long-term persuasion process, they offer more anonymity,

Among emerging—persuasive—technologies, the wearable ones have the potential to build motivation by increasing health awareness through timely feedback [24]. A multidisciplinary approach in developing wearable technology based on biology, physiology, nanotechnology, material science and other numerous disciplines is influencing rapid increase both in product and in research development. These devices are often based on sensing technology, communi-

So far, in the field of health, the wearable technologies have predominately focused on diet and physical activity, such as weight loss, motivating physical activity, maintaining exercise routines and an overall better understanding of an individual's health. In the commercial sector, one of the earlier more successful devices includes Nike+iPod (nikeplus.com), which employs a sensor in the runner's shoe and an iPod to keep track of the current pace and distance of a workout. In addition to tracking personal workout statistics, the runner can connect to an online community through the Nike+ website where he/she can track goals, compare running times with others and challenge friends. While Nike+ is designed for the specific activity of running, FitBit (fitbit.com), BodyMedia Fit (body-media.com) and Jawbone UP (jawbone.com) track everyday activities like walking (steps taken), climbing stairs (number of floors) and sleeping (hours and quality of sleep). Much like Nike+, all these wearable devices sync with an e-health application through a wireless base station. Additionally, they provide users with the ability to log individual foods and workouts from a smart phone or computer to track diet and exercise. BodyMedia Fit is geared towards weight loss and is worn as an armband, while FitBit and UP focus on smaller lifestyle changes and are clipped to the waist and worn on the wrist, respectively.  Beside previous examples, wearable systems can be designed with a purpose to provide more control to the users and focus on more health consciousness increasing applications rather than just monitoring because presumably the prevention and health sector is one of the strongest

There are activities and environments for which are required external incentives and motivations. As regards, the objective of our research project was in designing a protective and preventive wearable system that consists basically in monitoring of the user's physiological parameters (1), environmental monitoring and hazard detecting (2), real-time feedback pro-

Since when designing wearable technologies it is very important to focus on user's needs, development of research project was funded on human-centred approach, aimed at meeting not only technological requirements but also and especially user's needs and meaningful experience.

and they arrive at the contexts where human beings might not arrive.

cation and persuasion [25].

52 Wearable Technologies

areas of innovation [26].

3. Method

viding (3) and allowing data transmission.

User doesn't really understand the advantages of wearing such devices because often these devices are perceived as uncomfortable and alien to users. This occurs especially when the requirements referring to the formal and ergonomic characteristics are not well accomplished well as adaptability and adhesion to the body and dimensions of the housing [28]. There are different theoretical definitions about comfort, but all of them embrace the concept of creating harmony between the individual and environment and avoiding unpleasantness on all levels of existence. Any good design should consider both a user's need—as in this case is the comfort and body anatomic—and also the aspiration—which would be the designer's knowhow. In this way it is possible to design devices that meet both perceptual needs and needs related to wearability of the user [29].

The word "wearability" literally means ability to wear and concerns the physical shape of wearables and their active relationship with the human form. The target is to define the correct/pleasant interaction between the human body and the wearable object by trying to figure out a flexible shape without interfering with human daily activity [30].

Applying human-centred approach in designing means digging in worker's behaviour, perception, working environment and generally social aspects of the work. This does not exclude identifying of product's technical requirements and issues related to wearable technologies. Merging these two in designing process makes possible the achievement of an appropriate design solution both from the point of technological adeptness and general comfort related to wearability [31].

In the development of the new wearable system, authors had to take into account the product language and the correct pleasant interaction between the product and the user but also to design for motivation. Indeed, authors found important emphasising the factor of influencing more responsible users' behaviour towards their health conditions through the method of personal and environmental monitoring. Introducing the technology and technological solutions in this context is a way to provide objective data to the user and raise awareness and motivation.

When designing devices for persuasion, there is also a high demand on the way the feedback is transmitted to the user. For example, the reason why health awareness is accomplished with this kind of devices or applications is because it is in the real time—the gap between action and information (feedback) is denied [24].

For some applications, there is the necessity to create a real-time feedback. If we need a device to persuade on longer periods, feedback out of the context is a valid consideration. One of the often-used methods for creating persuasive technologies is personalisation. Personalised products are more likely to be accepted by the users. Designing personalised product does not have to be realistically addressed to a single person, but that it gives a feeling of being personal [2].

In the following paragraphs, the authors show the design process applied in this research project and development that consists in few phases: (1) the Empathise phase based on design thinking approach and user centred; (2) the Define phase where the project brief was imposed; (3) the Ideation phase in which concepts were developed, followed by the concept evaluation; (4) the Developing phase that is the development of the chosen concept and finally (5) the testing phase to figure out if the new wearable system satisfies user demands and project objective.

long time in the environment without being used. Reusable mask should be placed in a

Wearable Technology as a Tool to Motivate Health Behaviour: A Case Study

http://dx.doi.org/10.5772/intechopen.77002

55

From 1 h observation (Figure 1), it was observed that the workers are wearing PPE only occasionally when there is a presence of overspray that is mostly present in complex geometry painting. These findings brought us to the conclusion that the decision of wearing the PPE and the frequency of wearing it is strongly dependent on subjective perception (principally olfac-

Even though not all companies have the same equipment and typology of the activity, helpful conclusions were brought from both phases of user analysis. Observation phase results were reinforced with those gathered from interviews which confirmed all our doubts. The Interviews were organised under a few topics: Working activity and protective equipment; Mask's aesthetic and comfort; Safety perception; Personal devices and executed with ten workers (four

All interviewees stated that the decision about wearing the mask is autonomous and personal and that the mask is stored in a drawer when not used. Since the decision of wearing the mask is personal, it is based on the subjective feeling of the worker, or rather on olfaction perception. This causes sparse wearing of the mask that considers time that does not exceed 1.5 h per day. The level of odour presence and overspray is dependent both on the geometry of painted piece and on efficacy of aspiration system. Workers do not believe that the PPE is not useful; they also know that they are suggested to wear it, but they simply do not perceive well the

Beside low comprehension about PPE usage due different factors, workers consider that the mask is uncomfortable because it is too rigid, not enough breathable space and laces for regulations are rather bothering due to being too tight. Workers suggested that the application of new materials and particular attention in designing details may increase wearability and comfort of the mask. Moreover, we wanted to investigate also the safety perception of workers, where only 8 of 20 claimed to be aware of it and understood that the risk comes from the hazards. Additional information that we obtained confirm that according to workers protection level of the mask is good and that they are positive about having information both about personal health parameters and environmental ones that may consider implementation of sensors and advanced technology. With regard to this, workers were delighted about the idea of integration of sensor in

from the first, four from the second and two from the third company).

situations for which and for how long it is necessary to wear it.

protected place and closed when not used.

tory or visual).

Figure 1. Workers in a cabin.

#### 3.1. Participants

For the user analysis, the choice of the proper participants was relevant. We decided to choose workers of coating plants. This choice is strategic for the project execution because workers of this environment are highly exposed to the inhalation of several damaging agents (volatile organic components, organic dust, chemical contaminants, paints, varnishes) that cause respiratory disease. Our research activity was supported by Anver (Associazione Italiana Verniciatura) in finding three SMEs available for participating in the user analysis and observation and helping us understand how the process of painting is being executed, who and how is performing this activity and what are the general conditions, rules and norms in coating plants. Three companies with less than twenty employees specialised in finishing and lacquering of metals, polymers, wood and steel were chosen.

#### 3.2. Identifying the users and general problematics

In order to understand the users and what their activity actually consists of we proceeded with organising the user sessions in all three companies. Sessions consisted of two phases: (1) observation; (2) semi-structured interviews. Observation was focused on identifying characteristics of the working activity performed by the worker, his/her behaviour and usage of PPE. Semi-structured interviews were done both with workers to understand the level of perception about the risk and what they find as motivating or demotivating about wearing the PPE, and employers with a purpose to understand how the question about providing and wearing the PPE in this kind of environment is regulated, and in both cases we wanted to understand whether they are positive about introducing advanced technology based on monitoring in their ambit. In this session, all 20 workers were engaged in the period between December 2015 and January 2016. The user session was essential for this research because it was necessary to get to know the users well, their behaviour, if their activity is supposed to take place in coating cabin and with an aspiration system or not and typology of PPE that they are provided. Findings about the PPE show that the workers are generally provided with two different types of masks: (1) Disposable Protective Mask (with a small valve that helps the respiration) and (2) Reusable Half Mask with carbon filters (solid or liquid particles gas and vapour); the selection of these is certainly dependent on technical and chemical parameters such as: Typology of coating, Typology of pollution, Density of Coating Powder. The mask has its expiry date and it is dependent on its typology. Disposable masks last one day and have to be immediately thrown away, while the reusable ones have temporary bodies and filters that are interchangeable and last no more than 1 month. The filter expiring frequency is not dictated only by the hours of wearing but also whether it was protected in the box or exposed long time in the environment without being used. Reusable mask should be placed in a protected place and closed when not used.

From 1 h observation (Figure 1), it was observed that the workers are wearing PPE only occasionally when there is a presence of overspray that is mostly present in complex geometry painting. These findings brought us to the conclusion that the decision of wearing the PPE and the frequency of wearing it is strongly dependent on subjective perception (principally olfactory or visual).

Even though not all companies have the same equipment and typology of the activity, helpful conclusions were brought from both phases of user analysis. Observation phase results were reinforced with those gathered from interviews which confirmed all our doubts. The Interviews were organised under a few topics: Working activity and protective equipment; Mask's aesthetic and comfort; Safety perception; Personal devices and executed with ten workers (four from the first, four from the second and two from the third company).

All interviewees stated that the decision about wearing the mask is autonomous and personal and that the mask is stored in a drawer when not used. Since the decision of wearing the mask is personal, it is based on the subjective feeling of the worker, or rather on olfaction perception. This causes sparse wearing of the mask that considers time that does not exceed 1.5 h per day. The level of odour presence and overspray is dependent both on the geometry of painted piece and on efficacy of aspiration system. Workers do not believe that the PPE is not useful; they also know that they are suggested to wear it, but they simply do not perceive well the situations for which and for how long it is necessary to wear it.

Beside low comprehension about PPE usage due different factors, workers consider that the mask is uncomfortable because it is too rigid, not enough breathable space and laces for regulations are rather bothering due to being too tight. Workers suggested that the application of new materials and particular attention in designing details may increase wearability and comfort of the mask. Moreover, we wanted to investigate also the safety perception of workers, where only 8 of 20 claimed to be aware of it and understood that the risk comes from the hazards. Additional information that we obtained confirm that according to workers protection level of the mask is good and that they are positive about having information both about personal health parameters and environmental ones that may consider implementation of sensors and advanced technology. With regard to this, workers were delighted about the idea of integration of sensor in

Figure 1. Workers in a cabin.

In the following paragraphs, the authors show the design process applied in this research project and development that consists in few phases: (1) the Empathise phase based on design thinking approach and user centred; (2) the Define phase where the project brief was imposed; (3) the Ideation phase in which concepts were developed, followed by the concept evaluation; (4) the Developing phase that is the development of the chosen concept and finally (5) the testing phase to figure out if the new wearable system satisfies user demands and project

For the user analysis, the choice of the proper participants was relevant. We decided to choose workers of coating plants. This choice is strategic for the project execution because workers of this environment are highly exposed to the inhalation of several damaging agents (volatile organic components, organic dust, chemical contaminants, paints, varnishes) that cause respiratory disease. Our research activity was supported by Anver (Associazione Italiana Verniciatura) in finding three SMEs available for participating in the user analysis and observation and helping us understand how the process of painting is being executed, who and how is performing this activity and what are the general conditions, rules and norms in coating plants. Three companies with less than twenty employees specialised in finishing and lacquer-