*Article* **Processing Chain for Localization of Magnetoelectric Sensors in Real Time**

**Christin Bald and Gerhard Schmidt \***

Digital Signal Processing and System Theory, Institute of Electrical Engineering and Information Technology, Faculty of Engineering, Kiel University, 24143 Kiel, Germany<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>Tel.: +49-431-880-6125

**Abstract:** The knowledge of the exact position and orientation of a sensor with respect to a source (distribution) is essential for the correct solution of inverse problems. Especially when measuring with magnetic field sensors, the positions and orientations of the sensors are not always fixed during measurements. In this study, we present a processing chain for the localization of magnetic field sensors in real time. This includes preprocessing steps, such as equalizing and matched filtering, an iterative localization approach, and postprocessing steps for smoothing the localization outcomes over time. We show the efficiency of this localization pipeline using an exchange bias magnetoelectric sensor. For the proof of principle, the potential of the proposed algorithm performing the localization in the two-dimensional space is investigated. Nevertheless, the algorithm can be easily extended to the three-dimensional space. Using the proposed pipeline, we achieve average localization errors between 1.12 cm and 6.90 cm in a localization area of size 50 cm × 50 cm.

**Keywords:** localization; magnetoelectric sensors; real time; pose estimation

**Citation:** Bald, C.; Schmidt, G. Processing Chain for Localization of Magnetoelectric Sensors in Real Time. *Sensors* **2021**, *21*, 5675. https:// doi.org/10.3390/s21165675

Academic Editor: Daniel Ramos

Received: 30 July 2021 Accepted: 20 August 2021 Published: 23 August 2021

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#### **1. Introduction**

For the correct solution of inverse problems, such as source reconstruction of biomedical sources, it is essential to know the exact position and orientation of the measuring sensors with respect to the source besides measuring the biomedical signals. Especially in magnetic measurements the sensors do not necessarily have a fixed position and orientation. Thus, a determination of the position and orientation at the beginning of a measurement is not sufficient. Much more desirable is a continuous estimation of the sensor's position and orientation simultaneously with the measurement [1,2].

Magnetic tracking systems are used in many applications, e.g., in indoor positioning systems [3,4] or to locate medical devices inside the body [5,6]. Moreover, magnetic localization approaches are used to determine the position of the subject relative to the sensor array in biomagnetic measurements. A procedure for determining the subject relative to the measuring sensor array, either once at the beginning or also simultaneously with the measurement, was presented in [7]. In [1,2] a method for determining the positions of the individual sensors in a flexible on-scalp MEG system relative to the subject was investigated, which can also be applied during measurement.

Until now, mainly SQUIDs (Super Conducting Quantum Interference Devices) [8] and recently OPMs (Optically Pumped Magnetometers) [9,10] are used for the measurement of biomagnetic signals. Unfortunately, these sensors require a magnetically well shielded environment and are therefore inconvenient in operation. Magnetoelectric sensors, on the other hand, do not require any shielding, no expensive cooling system, and are also very small in size, which makes them ideal for array applications. The sensors are composed of a magnetostrictive and a piezoelectric layer and use the resonant structure of a cantilever [11]. Detection limits in the sub-nT regime have been reached recently [12–15] using modulation techniques such as the ΔE-effect [16] for the detection of low-frequency magnetic fields. Consequently, magnetoelectric sensors could be a promising alternative for biomagnetic field measurements in the near future [17].

Since the feasibility of measurements with simultaneous localization using magnetoelectric sensors has been shown in a previous work [15], this work will focus on the processing chain for an enhanced localization of magnetoelectric sensors in real time. However, by adapting the coil excitation signals the method can be used for arbitrary magnetic field sensors. This contribution will step through the general processing overview shown in Figure 1.

**Figure 1.** General overview of a medical system operating with magnetic sensors. The measurements are performed simultaneously with localizing the sensors. After transforming the signals into the digital domain, the signals are processed and analyzed. Since the analysis of the measured magnetic signals is not in the focus of this contribution, the corresponding box is depicted in gray.

In Section 2, the magnetoelectric sensor used in this paper will be presented and characterized. After explaining the so-called forward problem in Section 3, the real-time localization approach will be presented in Section 4. The presented localization processing chain will be verified by measurements presented in Section 5. The paper closes with a conclusion and an outlook in Section 6.

#### **2. Magnetoelectric Sensor**

For the proof of principle an exchange bias ΔE-effect sensor as depicted in Figure 2a will be used in this contribution. A sensor of the same type has already been used in a previous work [15]. The sensor is based on a polysilicon cantilever with a size of 1 mm width, 3 mm length, and 50 μm thickness.  Further details about the fabrication process of the sensor can be found in [15]. The magnetic multilayer consists of ferromagnetic and antiferromagnetic layers, which ensure the self-biasing of the sensor and thus lead to a shift of the magnetization curve of the sensor [18]. Hence, the sensor can be operated without applying an external bias field, which is especially favorable regarding array applications. The sensor is connected to a low-noise JFET charge amplifier [19] and placed on a printed circuit board. The whole sensor system is encapsulated in a 2.1 mm thick brass cylinder for electrical shielding.

As shown in [15], the localization of the magnetoelectric sensor can be performed simultaneously with a measurement without loss of information or degradation of the signals. The first bending mode was used to localize the sensor, while an artificial heart signal was measured in the second mode using the ΔE-effect. Hence, also in this contribution only frequencies around the first bending mode will be used for the transmission of the localization signals. The amplitude and phase response around the first bending mode of the magnetoelectric sensor used are shown in Figure 2c. The characterization measurements have been performed in a magnetically, electrically, and acoustically shielded environment [11]. The magnitude and phase response of the sensor have been measured applying a magnetic field of *b*ac = 1 μT on the sensor's long axis. The performance of the sensor can be determined as described in [20]. The sensor has a resonance frequency of *f*r = 7.712 kHz and a −3 dB bandwidth of *bw*–3dB = 10.2 Hz. Since the brass cylinder

acts as a low-pass filter with a cut-off frequency of approximately *f*c ≈ 1.5 kHz [15], the sensor's performance will be improved when removing the brass cylinder. Nevertheless, the encapsulation is necessary for electrical shielding and acts as a mechanical protection. Moreover, the limit-of-detection in the first bending mode with brass cylinder is still sufficient, because the coils can simply emit higher field amplitudes. The maximum sensitivity of the sensor is reached when a magnetic field is applied on the sensitive axis of the sensor. However, the sensitive axis of the sensor is not necessarily equal to the long axis of the sensor. There can be a tilt *γ* between these two axes [15,21], which is visualized in Figure 2b.

**Figure 2.** (**a**) Exchange bias ΔE-effect sensor used in this study. The sensor is based on a cantilever of size 3 mm × 1 mm. The cantilever is placed on a printed circuit board and connected to a low-noise JFET charge amplifier [19]. The sensor is encapsulated by a brass cylinder for electrical shielding and mechanical protection. (**b**) Visualization of the relationship between the sensitive and the long axis of the sensor. (**c**) Magnitude and phase response of the sensor in the first bending mode applying a magnetic field of *b*ac = 1 μT. The sensor has a resonance frequency of *f*r = 7.712 kHz and a bandwidth of *bw*–3dB = 10.2 Hz.

#### **3. Forward Problem**

For the localization of the magnetoelectric sensor, coils are placed outside the localization area as shown in Figure 3. If the distance between the sensor and the coil is large enough, the magnetic field of the coil *i* at the sensor position*rs*(*t*) can be approximated by the field of a magnetic dipole [22]:

$$\vec{b}\_{i}(t, \vec{r}\_{s}(t)) = \frac{\mu\_{0}}{4\pi} \frac{\mathfrak{d}\vec{r}\_{\rm cs,i}(t) \left(\vec{m}\_{\rm c,i}(t)^{\rm T}\vec{r}\_{\rm cs,i}(t)\right) - \vec{m}\_{\rm c,i}(t) \left\|\vec{r}\_{\rm cs,i}(t)\right\|\_{2}^{2}}{\left\|\vec{r}\_{\rm cs,i}(t)\right\|\_{2}^{5}} \tag{1}$$

Here, *μ*0 is the permeability of vacuum, *m* c,*i*(*t*) the magnetic dipole moment of the coil *i*, and*r*cs,*i*(*t*) = *r*s(*t*) −*r*c,*i* the distance vector between the sensor at position*r*s(*t*) and the coil *i* at position*r*c,*i*. The superscript T denotes the transpose of the vector. The positions of the coils*r*c,*i* are fixed during the measurement and therefore time independent. In this study, *N*c = 6 coils have been used. The coils have an effective diameter of 2.6 cm and consist of about 350 turns of enameled copper wire with a wire cross section of 0.13 mm2. The impedances of the six coils, separated into magnitude and phase, are shown in Figure 4.

The signal measured by the sensor

$$u\_{\rm in}(t) = h\_{\rm s}(t) \* \left(\vec{d}\_{\rm s}^{\rm T}(t) \sum\_{i=1}^{N\_{\rm c}} \vec{b}\_{i}(t, \vec{r}\_{\rm s}(t))\right) \tag{2}$$

can be described as a voltage at the output of the sensor system. At the location of the sensor a superposition of the magnetic fields of the coils is present. Due to the directional characteristic of the sensor *d*s(*t*), only a part of the applied magnetic field is picked up. The conversion of magnetic field into voltage by the sensor system (including the charge amplifier) is described by the impulse response *h*s(*t*). Equation (2) is valid at least for the frequencies around the first bending mode [15]. For simplification, no noise sources are considered here.

**Figure 3.** Real (**a**) and schematic (**b**) measurement setup for the localization of magnetoelectric sensors. The coils are placed outside of the localization area and transmit orthogonal signals, which are measured by the sensor. The localization area (box bounded by white stripes in (**a**)) is of size 50 cm × 50 cm.

**Figure 4.** Coil impedances separated into magnitude and phase. In (**a**) the whole spectrum from 100 Hz up to 1 MHz is shown, so that the resonance of the coils can be seen. In (**b**) the frequency range is scaled to the frequency range of the excitation signals.

#### **4. Localization Processing Chain**

For the estimation of the sensor's position and orientation an inverse problem must be solved. The processing chain for solving this inverse problem is shown in Figure 5.

**Figure 5.** Processing chain for the localization of magnetoelectric sensors in real time. The input signal of the sensor is matched filtered with the equalized coil signals. The matched filter outputs at time lag zero are compared with the lead-field matrix entries. A first order Kalman filter smooths the estimated position-orientation-pairs over time to mitigate possible outliers.

For this purpose, the localization area is first divided into a discrete grid containing *N*p different position-orientation-pairs *P* = [*p*1,..., *pj* ,..., *pN*p ], with *pj* = [*r* T p,*j* , *d*- T p,*j* ] T consisting of a position vector*r*p,*j* (containing x, y, and z components) and an orientation vector *d*p,*j* (directivity described by roll *ϕ*, pitch *θ*, and yaw *ψ*). The lead-field matrix

$$A = \begin{bmatrix} a\_{1\times} \dots \omega\_{j\times} \dots \omega\_{N\_{\text{p}}} \end{bmatrix} = \begin{bmatrix} a\_{11} & \dots & a\_{1j} & \dots & a\_{1N\_{\text{p}}} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ a\_{i1} & \dots & a\_{ij} & \dots & a\_{iN\_{\text{p}}} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ a\_{N\_{\text{c}}1} & \dots & a\_{N\_{\text{c}}j} & \dots & a\_{N\_{\text{c}}N\_{\text{p}}} \end{bmatrix} \tag{3}$$

describes the forward problem for the defined position-orientation-pairs in *P*. That means, the lead-field matrix entry of row *i* and column *j* is defined as

$$a\_{i\dot{j}} = \vec{d}\_{\text{p},\dot{j}}^{\text{T}} \frac{\text{3 } \vec{r}\_{\text{cp},\dot{j}} \left(\vec{d}\_{\text{c},i}^{\text{T}} \vec{r}\_{\text{cp},\dot{j}}\right) - \vec{d}\_{\text{c},i} ||\vec{r}\_{\text{cp},\dot{j}}||\_2^2}{||\vec{r}\_{\text{cp},\dot{j}}||\_2^5} \tag{4}$$

and thus describes the influence of coil *i* on the sensor, if the sensor would have the position and orientation described by *pj* . The distance vector is defined as*r*cp,*ij* =*r*p,*j* −*r*c,*i* and the orientation vector *d*p,*j* can be described by the angles *θ* and *ψ* (*ϕ* is always zero here) using [23]:

$$\vec{d}\_{\mathbb{P}^j} = \begin{pmatrix} \cos(\theta)\cos(\psi) \\ \cos(\theta)\sin(\psi) \\ -\sin(\theta) \end{pmatrix} \tag{5}$$

Equation (4) is a reduced magnetic dipole equation. The prefactor of Equation (1) is neglected and the magnetic dipole moment of the coil is reduced to the orientation of the coil.

#### *4.1. Signal Generation and Equalizer*

To separate the mixed signals received by the sensor, the signals of the coils must be orthogonal. Two signals *xi*(*n*) and *xj*(*n*) are orthogonal for *n* ∈ {*L*1, ... , *L*2}, if the following condition

$$\sum\_{n=-L\_1}^{L\_2} \mathbf{x}\_i(n) \mathbf{x}\_j^\*(n) \;= \begin{cases} E\_{i\prime} & i = j \\ 0, & i \neq j \end{cases} \tag{6}$$

is fulfilled [24]. Extending this equation to the constant *Ei* being 1, the signals are called orthonormal [24]. This is necessary to extract the individual coil amplitudes from the sensor signal and make them comparable. Different approaches can be used for the generation of orthogonal signals, e.g., using a TDMA (Time Division Multiple Access), an FDMA (Frequency Division Multiple Access) or a CDMA (Code Division Multiple Access) approach [25]. Due to the small bandwidth of the sensor, a TDMA approach is used in this contribution. Thus, the excitation signals

$$\mathbf{x}\_{\rm ex}(n) = \cos(2\pi f\_{\rm r}(n - \kappa\_i)) \,\mathrm{w}(n - \kappa\_i) \quad \text{with } \kappa\_i = (i - 1)L\_{\rm mf} \quad \forall i \in \{1, \ldots, N\_{\rm c}\} \tag{7}$$

are cosine signals at the resonance of the magnetoelectric sensor [15]. The signals are weighted with a Hann window *w*(*n*) [26] of length *L*sig, so that a smoothed in- and outfading of the signals is ensured. Additionally, the condition *L*mf ≥ *L*sig must be fulfilled. If *L*mf > *L*sig, there is a pausing time between two consecutive coil signals. This is important when considering the impulse response of the sensor. The excitation signals are repeated every *L*r = *N*c*L*mf samples

$$\mathbf{x}\_{\text{out},i}(n) = \mathbf{x}\_{\text{ex},i}(n - \lambda L\_{\mathbf{f}}) \quad \text{with } \lambda \in \mathbb{Z} \tag{8}$$

and transmitted by the coils after D/A conversion. It should be noted that *L*r = *L*mf if an FDMA or a CDMA approach is chosen, because the signals are transmitted simultaneously by all coils.

As can be seen from Equation (1) the magnetic field of a coil is proportional to the driving current. Since the output of the D/A converter is proportional to a voltage, the excitation signals *x*ex(*n*)=[*x*ex,1(*n*),..., *x*ex,*N*c (*n*)]T are linearly deformed in amplitude and phase. This can be described by the impulse response of the coil *h*c,*i*(*n*), denoting the relationship between the voltage and the current of the coil *i*. Additionally, the signals are modified by the impulse response of the magnetoelectric sensor *h*s(*n*), as can be seen from Equation (2). Thus, the signals *x*ex(*n*) must be equalized either prior to the deformation due to the coil and sensor impulse response or before they are forwarded to the matched filter. In this contribution, the matched filter impulse responses are adapted to match with the modified transmitted signals, so that they are again comparable to the coil signals measured by the sensor. Each coil excitation signal is adjusted individually by the equalizer

$$h\_{\text{eq},i}(n) = \oint\_{\text{c},i} \hat{h}\_{\text{c},i}(n) \* \hat{h}\_{\text{s}}(n) \,. \tag{9}$$

with ˆ *h*c,*i*(*n*) and ˆ *h*s(*n*) denoting the approximated impulse responses of the coil *i* and the magnetoelectric sensor, respectively. The factor *g*ˆc,*i* describes the influence of other components in the measurement setup. This includes for example different gains of the individual coil amplifier channels and different conversion factors of the coils from current to magnetic field. This is, e.g., due to variances in the number of windings. These values are approximated by a constant for the considered frequency range. The values for the six coils and amplifier channels used in this study are given in Table 1.

**Table 1.** Parameter of the coils and amplifier channels used in this study. The conversion factors of the coils describing the relationship between current and magnetic field are described by the column *conversion*. The gains of the coil amplifier channels are normalized to the maximum value (channel 6). Both values are determined at the resonance frequency of the sensor.

The equalized signals are calculated according to

$$\mathfrak{x}\_{\mathsf{eq},i}(n) = \mathfrak{g}\_{\mathsf{eq},i} \underbrace{\mathfrak{x}\_{\mathsf{eq},i}(n) \* h\_{\mathsf{eq},i}(n)}\_{\mathfrak{x}\_{\mathsf{eq},i}(n)} \tag{10}$$

and forwarded to the matched filter. The weighting factor *g*eq,*i* = 1 *Ei* ensures that each equalized signal has the same correlation output value one between the signals *x*eq,*i*(*n*) and *x*˜eq,*i*(*n*) at lag zero. The factor *Ei* is the auto-correlation output of *x*˜eq,*i*(*n*) at lag zero. In Figure 6 the cross correlation of the signals *x*eq,*i*(*n*) and *x*˜eq,*i*(*n*) at time lag zero is visualized.

**Figure 6.** Cross correlation between individual equalized coil excitation signals.

It is obvious that adjacent coils signals have cross correlation values different than zero. This is due to the decay behavior of the sensor impulse response. Nevertheless, the shown values are still sufficient for separating the coil signals.

#### *4.2. Matched Filter*

As described in Equation (2), the input signal of the sensor is a superposition of the magnetic coil signals. Additionally, noise sources superimpose the signal. To obtain a high signal-to-noise ratio (SNR) the coil amplitudes can be increased, which leads to a high energy consumption. Alternatively, a matched filter [27] can be used, which increases the SNR and thus can perform well also with lower energy consumption. This additionally makes the algorithm more robust against distortions. Hence, to obtain the amplitudes of the coil signals measured by the magnetoelectric sensor, the sensor input signal *x*in(*n*) is matched filtered with the equalized coil excitation signals

$$\mathbf{x}\_{\rm mf,i}(n) = \mathbf{x}\_{\rm in}(n) \* \mathbf{x}\_{\rm eq,i}(L\_{\mathbf{r}} - n) \,. \tag{11}$$

The matched filter output can be evaluated every *L*r samples, since all coil signals have then been completely transmitted once

$$m\_i(k) \, = \, \text{x}\_{\text{mf},i}(kL\_\mathbf{r}) \,. \tag{12}$$

with the value *mi*(*k*) corresponding to the amplitude of the coil signal *i* at the sensor. These amplitudes are summarized in the vector *m*(*k*) = [*m*1(*k*), *m*2(*k*),..., *mN*c (*k*)] T and forwarded to the localization algorithm.

#### *4.3. Localization Algorithm*

For the estimation of the position and orientation of the sensor, the matched filter output vector *m*(*k*) is compared with the columns of the lead-field matrix *A*. As stated in Equation (4), each column *aj* describes the coil amplitudes that would be measured by the sensor (after being filtered by the matched filter), if the sensor would occupy the defined position and orientation pair described by *pj* . To be more robust against gain uncertainties and to ensure comparability between the measured coil amplitudes and the lead-field matrix columns, the vectors are normalized to the respective absolute maximum value beforehand. The values for the cost function *c*(*k*)=[*c*1(*k*), ... , *cj*(*k*), ... , *cN*p (*k*)]T are calculated by [15]:

$$c\_{j}(k) = \sum\_{i=1}^{N\_{\mathcal{C}}} \left| \frac{a\_{ij}}{\max\left\{ |a\_{j}| \right\}} - \frac{m\_{i}(k)}{\max\left\{ |m(k)| \right\}} \right|^{2} \forall j \in \{1, \ldots, N\_{\mathcal{P}}\} \tag{13}$$

The estimated sensor position and orientation is then given by the position-orientation pair of the forward problem with the minimum cost function value

$$\mathfrak{p}(k) = \mathfrak{p}\_{l(k)} \text{ with } l(k) = \underset{j}{\text{argmin}} \ c\_{j}(k) \tag{14}$$

and can also only be determined every *L*r samples. It is obvious that localization errors occur due to the discretization of the localization area. If the sensor is not directly located on a defined grid point, the localization error will be at least the distance between the closest grid point and the sensor's location. Unfortunately, even higher localization errors can occur in some forward model configurations, due to the shape of the cost function *c*(*k*). Further information can be found in Appendix A. To overcome this problem a higher resolution is required. However, this will increase the computational complexity dramatically and hence can endanger the real-time capability of the system. By increasing the resolution iteratively, the localization can be performed with high accuracy and a moderate increase in computational complexity.

The flow chart for the iterative localization process is shown in Figure 7. The first iteration is calculated as described before. Instead of considering only one estimated position-orientation-pair as stated in Equation (14), the *N*b best position-orientation-pairs are taken into account. The minima and maxima of the included position-orientation-pairs plus a fraction (one step size) of the previous grid form the boundaries of the new grid. The new grid is again divided into *N*p position-orientation-pairs and the forward problem as described in Equation (4) is calculated. From then on, the steps are repeated as described in the upper part of this section. Grid refinement stops when either the resolution between

two adjacent grid points is less than a specified resolution or the maximum number of iterations *N*it has been reached. The estimated position-orientation pair is given in the last iteration as described by Equation (14).

**Figure 7.** Flow chart of the iterative localization approach for one time step *k*. As long as neither the maximum number of iterations nor the desired resolution is reached, the algorithm keeps refining the localization grid.

#### *4.4. Postprocessing*

To mitigate possible outliers in the localization results, a linear Kalman filter for smoothing the estimated localization outcomes is used. The measurement equation of the system can be described by

$$
\mathfrak{p}(k) = \mathrm{Hs}(k) + \mathfrak{n}\_{\mathrm{m}}(k), \tag{15}
$$

with the state variables *s*(*k*), the observation model *H* transforming the states into the measurement variables, and supposing white Gaussian measurement noise *n*m(*k*) [28]. Assuming a linear system, the state variables are updated via the equation

$$\mathbf{s}(k) = \mathbf{F}\mathbf{s}(k-1) + \mathfrak{n}\_{\mathbb{P}}(k) \,. \tag{16}$$

The matrix *F* is the state transition matrix and *n*p(*k*) is white noise with zero mean [28]. Due to the noise processes the measurement variables are subject to errors. The Kalman filter attempts to predict the states and thus reduces the influence of the noises stated in Equations (15) and (16). The calculations are performed according to the descriptions in [28]. Based on the *N*m = 6 measured variables summarized in *p*ˆ(*k*), there are *N*s = 3 · *N*m = 18 states available, when additionally considering the velocity and acceleration of the measured variables. The initialization of the variables and covariance matrices are described in Appendix B. The enhanced localization output is denoted as *p*ˆ enh(*k*). It is worth noting that the Kalman filter outputs should not lie outside the localization area and are thus restricted to the boundaries of the localization grid.

#### **5. Measurements and Results**

For the proof of principle, the measurements were performed in a two-dimensional space, i.e., only considering the x and y components of the sensor position and only considering the angle yaw *ψ* for the orientation of the sensor. The z component of the sensor's position, as well as the orientation angles roll *ϕ* and pitch *θ* are assumed to be zero. Nevertheless, the proposed method can easily be performed in the three-dimensional space without any restrictions. More coils should be used for this, positioned in the threedimensional space. Additionally, a smaller initial grid resolution might be used to keep the computational complexity low. The measurements were performed with a real-time system developed at the chair of Digital Signal Processing in Kiel [29]. A picture of the graphical user interface of the tool is shown in Figure 8.

**Figure 8.** Graphical user interface of the real-time system used for localizing the magnetic sensors. The estimated position and orientation of the sensor is shown graphically in the 3D view and as text in the lower left corner. The number of iterations *N*it, the number of considered position-orientation pairs for refining the localization grid *N*b and the desired resolution in position and orientation are adjustable during runtime.

> The parameter used for the measurements (as defined in the sections above) are listed in Table 2. The localization area is limited to values between 0 cm and 50 cm in x and y direction and between −90◦ and 90◦ for the yaw angle.

**Table 2.** Parameter of the coil signals used in this study.

The waiting time between two consecutive coil signals must be rather high due to the decay of the impulse response of the sensor. Consequently, a total time of *L*r *f*s = 896 ms, with *f*s being the sampling rate, is needed to completely transmit the signals and thus to generate one localization result. This time can be shortened tremendously when using a sensor with a higher bandwidth. The sensor is placed in fixed positions and with fixed orientations to determine the accuracy of the algorithm. The tested position-orientation pairs *p*s,*j* were chosen randomly and are shown in Figure 9. The arrow directions represent the sensor's long axis.

**Figure 9.** Position-orientation-pairs of the sensor used for testing the localization algorithm.

The tilt between the sensor's sensitive and long axis has been approximately determined by manually rotating the sensor in a Helmholtz coil. The tilt was measured outside a shielded environment and results in *γ* ≈ −45◦. However, the tilt of the sensor depends on various factors, such as the strength of the bias field [30] (e.g., the earth's magnetic field) and is therefore only an approximation.

The results of the localization for the position-orientation pair *ps*,3 over time are shown as an example in Figure 10. Here, *x*s,*j*, *y*s,*j*, and *ψ*s,*j* denote the x and y component and the yaw angle of the sensor at position *ps*,*j* , respectively. The localization output without the Kalman filter is described by *x*ˆs,*j*(*k*), *y*ˆs,*j*(*k*), and *ψ*ˆs,*j*(*k*) and after smoothing by the Kalman filter by *x*ˆenh s,*j* (*k*), *y*ˆenh s,*j* (*k*), and *ψ*ˆenh s,*j* (*k*).

**Figure 10.** Real and estimated position and orientation of *p*s,3 over time. The variances in the localization result are due to the presence of noise and cross talk in the measurement hardware.

There are some variances of the localization result over time. This is mainly due to the presence of noise, which leads to slightly varying amplitudes at the sensor and thus to small variations in the localization outcomes. The offset error might be due to cross talk between the coil amplifier channels and coupling of the magnetic coil signals into the cables and electronics. Additionally, it can be seen that the Kalman filter smooths the estimation output over time, so that outliers in the localization results do not have such a high impact.

To quantify the accuracy of the algorithm, a localization error is defined according to

$$\bar{\varepsilon}\_{\mathbf{r},j} = \frac{1}{N\_{\text{meas}}} \sum\_{k=0}^{N\_{\text{meas}}-1} \sqrt{\left(\hat{\mathfrak{x}}\_{\mathbf{s},j}^{\text{enh}}(k) - \mathfrak{x}\_{\mathbf{s},j}\right)^2 + \left(\hat{\mathfrak{y}}\_{\mathbf{s},j}^{\text{enh}}(k) - \mathfrak{y}\_{\mathbf{s},j}\right)^2} \tag{17}$$

for the position estimation and

$$\mathcal{E}\_{\rm d,j} = \frac{1}{N\_{\rm meas}} \sum\_{k=0}^{N\_{\rm meas}-1} \sqrt{\left(\hat{\Psi}\_{\rm s,j}^{\rm csh}(k) - \psi\_{\rm s,j}\right)^2} \tag{18}$$

for the orientation estimation. The number of localization outcomes is set to *N*meas = 50, according to a measurement time of 44.8 s. The accuracy of the localization results for all tested position-orientation-pairs is shown in Figure 11.

The localization error is lying between 1.12 cm and 6.90 cm for the position estimation and results in a mean error of about 3.44 cm. The error for the orientation estimation is between 3.02◦ and 16.76◦. This results in a mean error of about 11.23◦. When considering fixed positions and orientations of the sensor, the localization output can be averaged over time and compared to the real sensor position/orientation afterwards. When doing so, the localization accuracy can be slightly improved and results in values between 0.46 cm and 6.52 cm for the position estimation and 1.54◦ and 15.35◦ for the orientation estimation. The average error reduces to 3.14 cm and 10.54◦, respectively.

The high errors for the estimation of the sensor's position can result from the noise and the cross talk in the measurement system. Due to the different distances between the coils and the sensor the SNR is dependent on the sensor position/orientation. For example,

looking at position *ps*,9 an average SNR of about 9.5 dB is obtained at the sensor. Higher coil currents would lead to an improved SNR. Nevertheless, the goal was to localize with a minimum amount of energy. To avoid cross talk, the cables as well as the amplifier channels must be shielded. Additionally, the remaining cross talk can already be considered when setting up the forward problem or with an appropriate initial calibration. However, since the focus of this work is on the real-time localization pipeline and the calibration will be very extensive, it is not the subject of this work, but will be taken into account in our future work. The high error variance of the orientation estimation can partly be due to the change of the sensor's sensitive axis with respect to the bias field. Even a rotation in the earth's magnetic field can tilt the sensitive axis [30]. This problem only occurs with the sensors presented here and not with other types of magnetic field sensors. Furthermore, possible calibration errors do not only influence the position estimation but also the orientation estimation.

**Figure 11.** Mean localization errors for all tested position-orientation pairs. The index *j* depicts the respective position-orientation pair *p*s,*j* of the sensor as defined in Figure 9.

#### **6. Conclusions and Outlook**

An algorithmic pipeline for localizing magnetic sensors in real time was presented in this contribution. Besides the localization algorithm itself, pre- and postprocessing steps for an enhanced estimation of the sensor's position and orientation have been described. The potential of the proposed algorithm was emphasized by measurements with a magnetoelectric exchange bias ΔE-effect sensor. Nevertheless, the proposed method can be applied to any type of magnetic field sensor. Only the coil excitation signals must be adapted to the properties (frequency range, dynamic range, etc.) of the magnetic sensor used. Using the magnetoelectric sensor, a mean localization error of 3.44 cm has been reached. For the proof of principle the localization of the sensor has been limited to the two-dimensional space. Nevertheless, the localization can be easily extended to the three-dimensional space.

The achieved results are comparable with other magnetic position estimation approaches. In [4]a3 × 3 m grid was used for localizing, achieving an accuracy of less than 10 cm. Localizing with a 3D sensor in a grid size of 8 × 7 cm an accuracy of 2.6 mm could be reached in [31]. In [2] a localization accuracy of ≤ 2 mm has been achieved, using coils for the localization of the sensors in a flexible MEG system. That shows that our localization method performs well, but can still be improved. However, the localization method investigated in this contribution can be performed in real time and in parallel to magnetic measurements without any degradation. Additionally, the robustness in noisy environments is increased by the usage of a matched filter and the smoothing of the localization outcomes via the linear Kalman filter. Moreover, the usage of a magnetoelectric sensor can be advantageous with respect to later medical applications due to its small size and the low production and operation costs.

Until now, the biggest limiting factor is the hardware used. Moreover, only a simplified model of the magnetoelectric sensor has been used, where the sensor is reduced to a point model. A more detailed model of the sensor, which also considers the dimensions of the sensor as well as a bias field dependent tilting of the sensor's sensitive axis, could improve the localization accuracy. The results can be further improved using multiple sensors included in an array with fixed distances and orientations as described in [1]. To reduce the transmitting time of the coils and thus to increase the rate of localization outcomes, FDMA or CDMA approaches would be beneficial. This would require an adaption of the sensor hardware.

**Author Contributions:** Conceptualization, C.B. and G.S.; methodology, C.B. and G.S.; software, C.B.; validation, G.S.; formal analysis, C.B.; investigation, C.B.; writing—original draft preparation, C.B.; writing—review and editing, C.B. and G.S.; visualization, C.B.; supervision, G.S.; project administration, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) via the collaborative research center CRC 1261.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank Alexander Teplyuk for building the coil amplifier and Christine Kirchhof, Phillip Durdaut, and Jens Reermann for building up the sensor.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **Appendix A. Localization Errors**

As already mentioned in Section 4.3, localization errors can occur due to the discretization of the localization area. It is obvious that the minimal localization error is defined between the real sensor position/orientation and the closest grid point and is only zero, when the sensor is lying directly on a defined grid point. Besides these obvious errors even higher localization errors can occur, if the grid resolution is not high enough. This highly depends on the coil arrangement, i.e., on the setup of the forward problem. In Figure A1 the cost function of a sensor located at point *A* (only considering a 2D plane and assuming a fixed orientation) is shown assuming an exemplary (not well set up) forward problem.

Now assuming a localization grid with a coarse resolution, which is visualized by the black lines. The points, where the lines are crossed are the potential grid positions. The minimal localization error would be the distance between point *A* and point *B*. However, the position-orientation pair with the smallest cost function is found at point *C*. This is due to the shape of the cost function and the small grid resolution, whereby the grid points do not lie on the minimum of the cost function. This results in a large localization error. Thus, a higher grid resolution is needed, which leads to a higher computational complexity and hence can endanger the real-time capability of the system. To overcome this, an iterative localization is implemented, which refines the grid resolution progressively.

**Figure A1.** Exemplary cost function. The sensor is located at point *A*. Due to the relatively coarse grid (black lines), there will be a localization error of at least the distance between the point *B* and point *A*. However, due to the shape of the cost function, the minimum that is crossing the grid lines—and thus the localization outcome—is at point *C*.

#### **Appendix B. Initialization of the Kalman Matrices**

As already stated in Section 4.4 the calculations of the Kalman filter are performed as described in [28]. The initialization of the matrices is filled as described for a discrete Wiener process acceleration model [28].

• State transition matrix

$$F = \begin{bmatrix} I\_{N\_m} & \Delta t I\_{N\_m} & \frac{1}{2} \Delta t^2 I\_{N\_m} \\ \mathbf{0}\_{N\_m \times N\_m} & I\_{N\_m} & \Delta t I\_{N\_m} \\ \mathbf{0}\_{N\_m \times N\_m} & \mathbf{0}\_{N\_m \times N\_m} & I\_{N\_m} \end{bmatrix} \tag{A1}$$

• Measurement matrix

$$H = \begin{bmatrix} I\_{N\_{\mathrm{m}}}, \mathbf{0}\_{N\_{\mathrm{m}} \times 2N\_{\mathrm{m}}} \end{bmatrix} \tag{A2}$$

• Covariance matrix of the process noise

$$\mathbf{Q} = \mathbb{E}\{\boldsymbol{\mathfrak{w}}\_{\mathbb{P}}\boldsymbol{\mathfrak{w}}\_{\mathbb{P}}^{\mathrm{T}}\} = \begin{bmatrix} \frac{\boldsymbol{\Lambda}^{4}}{4} \boldsymbol{I}\_{N\_{\mathrm{m}}} & \frac{\boldsymbol{\Lambda}t^{3}}{2} \boldsymbol{I}\_{N\_{\mathrm{m}}} & \frac{\boldsymbol{\Lambda}t^{2}}{2} \boldsymbol{I}\_{N\_{\mathrm{m}}}\\ \frac{\boldsymbol{\Lambda}t^{3}}{2} \boldsymbol{I}\_{N\_{\mathrm{m}}} & \frac{\boldsymbol{\Lambda}t^{2}}{2} \boldsymbol{I}\_{N\_{\mathrm{m}}} & \boldsymbol{\Lambda}t \boldsymbol{I}\_{N\_{\mathrm{m}}}\\ \frac{\boldsymbol{\Lambda}t^{2}}{2} \boldsymbol{I}\_{N\_{\mathrm{m}}} & \boldsymbol{\Lambda}t \boldsymbol{I}\_{N\_{\mathrm{m}}} & \boldsymbol{I}\_{N\_{\mathrm{m}}} \end{bmatrix} \boldsymbol{\mathcal{O}}\_{p}^{2} \tag{A.3}$$

• Covariance matrix of the measurement noise

$$\mathcal{R} = \mathbb{E}\{\mathfrak{n}\_{\rm m}\mathfrak{n}\_{\rm m}^{\rm T}\} = I\_{N\_{\rm m}}\mathfrak{d}\_{\rm m}^{2} \tag{A4}$$

• State covariance matrix

$$\mathbf{S}(0) = \mathbf{I}\_{3N\_m} \boldsymbol{\vartheta}\_{\mathbf{s}}^2 \tag{A5}$$

Here, *IM*1 denotes a unit matrix of size *M*1 × *M*1 and **0***M*1×*M*2 a matrix filled with zeros of size *M*1 × *M*2. E{...} is the expected value operator and Δ*t* = *L*r *f*s , with *f*s being the sampling rate. Considering the high measurement noise and assuming a slowly moving or fixed sensor (i.e., low process noise), the estimated variances are set to *σ*ˆ 2 *p* = 0.001, *σ*ˆ 2 *m* = 10, and *σ*ˆ 2 *s* = 500. These values were chosen exemplary.

#### **References**


*2.6. Analytical Determinations*

Selenium, iron, manganese and aluminum concentrations were determined by using an atomic absorption spectrophotometry, Varian—spectraAA-300system. For the determination of Se at low concentrations, a Varian model VGA77 hydride generator was used. Available phosphorus concentrations were determined by a Shimadzu UV-1700 spectrophotometer. Every 10 samples a control sample was analyzed, and at the end of the measurements procedure 30% of the samples were reanalyzed to test reproducibility.

## *2.7. Statistics*

Correlation and *t*-test analysis (*p* < 0.05) were performed using STATISTICA 10 software (StatSoft Inc., Tulsa, 74104 OK, USA).

#### **3. Results**

### *3.1. Soil Properties*

The physicochemical properties of the studied soils and the total Se concentrations are summarized in Table 1. Most of the soils are characterized as medium to fine textured with low organic carbon content, as expected for Mediterranean agricultural soils, and with very low total Se concentrations, less than 0.28 mg kg−1, pointing to Se deficiency [33]. Ammonium oxalate and dithionite extractable Fe, Al and Mn are expressed as % oxides content (g 100 g−1 soil) and presented as Feo, Alo, Mno and Fed, Ald and Mnd, respectively. Metal oxide concentrations greatly varied, ranging between 0.08 and 0.40% and 0.72 and 6.32% for Feo and Fed, between 0.55 and 1.03% and 0.06 and 0.26% for Alo and Ald and between 0.01 and 0.10% and 0.02 and 0.15% for Mno and Mnd. The pH range of both alkaline and acid soils was very narrow—7.4 to 7.8 for alkaline soils and 5.5 to 6.0 for acid soils. Electrical conductivity values in the alkaline soils were significantly higher than in the acid soils (*p* < 0.001, *n* = 4), but were not restrictive for the growth of crops. Most soils were marginally to moderately supplied with available phosphorus.

**Table 1.** Soil physicochemical characteristics.


Experiment 2

Here, *ω*∗ is the sinusoidal trajectory (52). In Figure 12, the corresponding experimental results are depicted.

**Figure 12.** Experimental results for a sinusoidal trajectory. The results related to the measured variables correspond to the signals denoted by *ω*, *ia*, *υ*, *i*, whereas the results associated with the reference variables are labeled as *ω*∗, *i*∗*a* , *u*∗*av*, *υ*∗, *i*∗.


J. Santy-Tomlinson (\*)

K. Hertz

© The Editor(s) (if applicable) and the Author(s) 2018 137

L. Brent

National Office of Clinical Audit, St Stephen's Green, Dublin 2, Ireland

Faculty of Biology, Medicine and Health, Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK e-mail<EMAIL_ADDRESS>
Specialised Division, University Hospital of North Midlands, Stoke-on Trent, Staffordshire, UK e-mail<EMAIL_ADDRESS>
K. Hertz, J. Santy-Tomlinson (eds.), *Fragility Fracture Nursing*, Perspectives in Nursing Management and Care for Older Adults, https://doi.org/10.1007/978-3-319-76681-2\_11

sudden death but can also lead to an awareness that death is pending, but the process of dying may take several days or weeks. Patients and their families expect good end of life care that involves management of physical symptoms such as pain, breathlessness, nausea and increasing fatigue as well as the anxiety, depression and social and spiritual difficulties that can accompany the end of life. Whether the patient dies in the hospital or at home, care should be based on effective MDT working [1] that includes collaboration with the patient and their family and other informal carers.

This chapter will explore the importance of family partnerships in healthcare and approaches to palliative and end of life for care for patients with a fragility fracture.

# **11.1 Learning Outcomes**

At the end of the chapter, the nurse will be able to:

# **11.2 Family Partnerships and Involvement in Care**

There has been significant change in the way healthcare professionals view care delivery, from a medical-centric model to a patient-centred approach. The term "patient-centred medicine" was first coined in 1969 [2], and the Picker Commonwealth Program for Patient-Centred Care (subsequently the Picker Institute) developed the term "patient-centred" in 1998 [3]. It is now acknowledged that a key priority for patients is how their own wishes and those of their families and carers are integrated throughout the care process. Healthcare workers are now expected to place an emphasis on collaborating with patients and families of all ages, at all levels of care and in all healthcare settings.

The concept of family caregiving has also developed significantly and now reflects the changing nature of "family" in society. Much informal care is given by individuals who would not traditionally be considered family members and such "informal caregiving" is recognised as an important facet of care delivery. Informal caregivers are defined as "persons without formal health care education who are caring for, or helping, a person with functional disabilities, prolonged psychiatric or physical illness, or age-related problems" [4]. Within this chapter, the term "family"

will be used to encapsulate all persons who have an important relationship with the patient, e.g. relative, partner, friend and neighbour.

Families are an essential component of care, health and wellbeing; quality and safety initiatives recognise the role they play in ensuring high standards of care, and they are allies for quality and safety within the healthcare system [5]. Families often act as the primary caregivers and as advocates for patient who are not able to make decisions for themselves. They are an essential part of the patient care continuum and a fundamental characteristic of holistic care is that nurses must collaborate with others to achieve best practice [6].

There are four keys factors to be considered in relation to patient and family involvement: dignity and respect, information sharing, participation and collaboration. When a patient is admitted to a hospital or other healthcare setting, the reason for admission is often the focus; but, to be holistic, practitioners need to consider a myriad of factors. The reason the patient requires care may not be their own most significant concern. When asked, patients will tell healthcare staff what is worrying them most and it is up to practitioners to really listen and respect their wishes. Nurses spend the most time with the patient and therefore have a central role to play in relaying and advocating for the patient's and family's wishes and concerns, taking into consideration their knowledge, values, beliefs and cultural norms.

Communication is a key ingredient for ensuring timely and appropriate information sharing between healthcare staff and the patient and family. It is known that: "…no matter how knowledgeable a clinician might be, if he or she is not able to open good communication with the patient, he or she may be of no help" [7]; good communication also impacts on patient outcomes and healthcare staff experience. Patients' perceptions of the quality of the healthcare they receive are highly dependent on the quality of their interactions and communication with clinicians and team members [8]. All information shared should be unbiased and relevant; asking the patient or family what it is they need to know or better understand can help avoid frustrations, miscommunication, upset and worry. Practitioners must also communicate the same message to the patient and family to avoid misunderstandings and ensure that they are able to make the most appropriate decision about their care. This two-way dialogue encourages collaboration in achieving good quality decisionmaking and a better ultimate experience for all involved. Practitioners need to adopt a culture of collaboration with families and patients and have a clear programme and structure to ensure this happens at every level of the organisation.

# **11.2.1 Assessment**

In the first instance, a comprehensive holistic understanding of the family's role in the care of the patient needs to be achieved and documented. Principal issues for consideration include:

Families have other priorities such as work and other dependents such as children that need to be considered, and healthcare staff should never assume that the family wish or are able to give care, nor should judgements be made if they prefer not to be involved in direct care. It is important to establish how much support has been, and can be, given by non-family members, the costs associated and how effective this is. Family should be asked if they are aware of any voluntary or communitybased support that can provide resources. Grants and financial help may be available that patients and family can access from social care agencies or voluntary organisations.

The involvement of families in care after discharge requires careful planning and organisation during the discharge planning process, beginning with an open conversation with the patient and their family to ensure that everyone understands what the implications of decisions are. Arrangements should be made for equipment and facilities in the home. Families may need to develop specific care skills, and arrangements need to be in place for this so that they understand issues such as patient limitations and potential continuing progress towards recovery. Should a decision be made that the patient will be discharged permanently or temporarily to residential care, the implications for the family such as social and financial aspects need to be carefully considered with the help of social work practitioners.

# **11.2.2 Caregiver Burden**

Voluntary or informal caregiving is a choice and should not be an expectation. Practitioners must take account of the likelihood of caregiver burden, defined as "the physical, emotional and financial responses of a caregiver to the changes and demands caused by providing help to another person with a physical or mental disability" [9]. This is commonly exhibited by families and carers of patients with long-term chronic diseases or acute prolonged episodes of care that can impact physically, psychologically and socially on carers' health and quality of life and, therefore, on the quality of care provided.

Common features of caregiver burden include tiredness, emotional distress, conflicts, financial difficulties, not meeting the care needs of the patient and changes in the relationship between the patient and carer. A significant cause of these problems is lack of preparation for the caregiver role, poor communication, lack of understanding and appropriate training for the carer, lack of support or perceived support and poor awareness of how to access resources or to navigate health and social care systems.

There is a relationship between informal caregiver wellbeing and the physical and psychological health of the patient, so health and social care services need to ensure that carers are fully supported [10]. This can only be achieved with continued contact and communication. Regular reviews of the care requirements should take place that will enable the carer to be supported. There is a limit to what is reasonable and achievable through informal care and longer-term plans may need to be put in place if care needs are prolonged or carers are not able to cope due to care burden.

# **11.2.3 Legal and Ethical Considerations**

The patient is the key decision-maker in their care but, if a patient does not possess the capacity to make decisions about their care, practitioners must act in their best interests to determine who is the correct and legal person to make and inform any decisions made. To do this, the practitioner must understand the terms capacity, advanced healthcare directive (AHD) or living will and co-decision-maker.

Capacity is defined as the person's ability to understand, at the time that a decision is to be made, the nature and consequences of the decision to be made by him or her in the context of the available choices at that time [11]. An expression by a person of his or her will and preferences concerning treatment decisions that may arise if the person subsequently lacks the capacity to make such decisions is known as a living will or an advanced healthcare directive (AHD). The person may appoint someone (known as a co-decision-maker) to jointly or solely make decisions, through a legal, witnessed and documented process, on their behalf should they not possess the capacity to do so.

# **11.3 Palliative and End of Life Care**

For some patients, their fragility fracture, especially a hip fracture, may be an event that will hasten the end of life. Some patients, especially those who are already frail, may be unable to survive the physiological stress of the fracture and subsequent surgery. At these times, the principles of palliative care should be applied. The World Health Organization [12] defines palliative care as; "an approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and impeccable assessment and treatment of pain and other problems, physical, psychosocial and spiritual". The fundamental aims of palliative care include [12]:

It is important to note that surgery for hip fracture may remain the most effective way to manage pain for patients who are reaching the end of life, so the reasons for the decision to undertake surgery must be clearly explained to the patient and family and ethical decision-making employed as discussed earlier.

Palliative care is not limited by time and care should be delivered based on needs as they arise. It can take place in primary care, in acute hospitals and in long-term and hospice care facilities. More people are living longer with more co-morbidities and, unfortunately, the insult of a major fracture such as a hip fracture can see the patient's health decline and ultimately result in end of life. It is estimated that there were approximately 54.6 million deaths worldwide in 2011 and that 9% of those were due to injuries [13]. Men have a higher risk of mortality after a hip fracture, but women are also at substantial risk of death; this risk exceeds the lifetime risk of death from breast cancer, uterine cancer and ovarian cancer combined. Many patients who survive a hip fracture do not regain their pre-fracture functional level, and almost one third lose their independence [14]. Practitioners must be equipped for, and expect to deliver, end of life and palliative care in the orthopaedic and orthogeriatric setting routinely rather than as an exception.

When considering the philosophy of "end of life care", Dame Cicely Saunders said: "You matter because you are you, and you matter to the end of your life.  Her words reflect the human responsibility to care for others in a humanistic and compassionate way until the end of their life.

There are many responsibilities in end of life care, ranging from communicating with individuals and families about their care and preferences; to observing, discussing and recording any changes in condition and offering compassion and support. A broad range of care skills are needed along with awareness of the values

which underpin this philosophy of care. When providing end of life care, practitioners should [15]:

End of life and palliative care are not limited to the acute hospital and can be provided in a range of settings including the community, care homes and hospices. Practitioners need to be attuned to noticing when a person is nearing the end of life or actively dying. How the patient and family are communicated with during this phase of life will depend on the individual patient. As much as possible, this should be patient-led and the nurse should proceed with gentle, honest answers, using a language the person understands. If the patient is uncomfortable, or does not wish to talk about death, it is important to respect their wishes. It is crucial, however, to have sensitive conversations with families and carers to prepare them for impending death.

Good nursing care for those at the end of their life should include physical, emotional and psychological aspects of care along with spiritual support. The process of dying creates multiple emotions and feelings for all involved; the patient, family, carers and the care providers. It can be very stressful and complex. It is helpful to use tools to assist in identifying indicators that someone is approaching their end of life such as the Gold Standards Framework (GSF) [16] and the Palliative Performance Scale 2 (PPS). Nurses play a key role in helping the patient throughout this natural process. The gentle "winding down" at the very end of life can be very peaceful as the body starts to let go, so if the patient is distressed or restless, this can be disrupted.

# **11.4 Summary of Main Points for Learning**

• A patient's family and other people meaningful to them are important participants in the care process and need to be recognised as such. Good communication and collaboration with families can be central to achieving high-quality care and good patient outcomes.

# **11.5 Suggested Further Study**

• Find local and national guidance for palliative and end of life care, and use these to identify ways in which care might be improved with respect to these aspects of care.

# **11.6 How to Self-Assess Learning**

To identify learning achieved and the need for further study, the following strategies may be helpful:

# **References**

1. NICE (2011) End of life care for adults: quality standard 13. National Institute for Health and Care Excellence. https://www.nice.org.uk/guidance/qs13

2. Balint M et al (1969) Training medical students in patient-centered medicine. Compr Psychiatry 10(4):249–258

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# **12 Orthogeriatric Nursing**

Julie Santy-Tomlinson, Karen Hertz, and Magdalena Kaminska

Nursing is central to good care for the patient with a fragility fracture and makes a major contribution to positive outcomes. Nurses are the largest group of health professionals in the orthogeriatric team and they are the one group who are present for the full 24-hour span during hospitalisation. They are also most likely to work across organisational boundaries, acting as links between the patient's home and local community, the hospital, the outpatient/ambulatory setting and other organisations.

Nurses who work in the orthogeriatric setting must be able to clearly articulate their role and value so that they can inform patients, their families and other members of the MDT what to expect from them. No single healthcare profession can provide care to fragility fracture patients in isolation, but it is known that patients' outcomes are improved if there is full collaboration across all disciplines making up the "orthogeriatric" team [1] and patients with a fragility fracture have numerous complex care needs that need a team approach that includes skilled, compassionate nursing. Although this chapter is concerned with orthogeriatric nursing generally, it is impossible to ignore the fact that of all fragility fractures, hip fracture is the most significant injury: it is the most common reason for admission to an orthopaedic ward, accounts for much orthopaedic bed occupancy and is a large portion of the

K. Hertz

M. Kaminska

© The Editor(s) (if applicable) and the Author(s) 2018 147

J. Santy-Tomlinson (\*)

Faculty of Biology, Medicine and Health, Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK e-mail<EMAIL_ADDRESS>
Specialised Division, University Hospital of North Midlands, Stoke-on Trent, Staffordshire, UK e-mail<EMAIL_ADDRESS>
Faculty of Health Sciences, Department of Primary Health Care, Pomeranian Medical University, Szczecin, Poland

K. Hertz, J. Santy-Tomlinson (eds.), *Fragility Fracture Nursing*, Perspectives in Nursing Management and Care for Older Adults, https://doi.org/10.1007/978-3-319-76681-2\_12

total cost of all fragility fractures. It is also the most expensive fracture in terms of volume and unit costs. Complexity of patient needs, prevalence, number of bed days and cost means that the focus of inpatient care tends to relate predominantly to this category of injury. However, the principal skills and knowledge needed to look after hip fracture patients well must be applied across the management of all older patients with fractures and include all fundamental aspects of nursing care for the adult as well as highly specialised interventions for older people.

The aim of this chapter is to consider the nature of orthogeriatric nursing and to explore its theoretical, political and professional aspects.

# **12.1 Learning Outcomes**

At the end of the chapter, and following further study, the nurse will be able to:

# **12.2 Nursing**

Nursing is broad and complex, so defining nursing enables nurses to explain to patients, families and others what they can expect from them. The ICN [2] defines both "nursing" and "a nurse" (see Box 12.1) to highlight the expanse of the nursing role across the entire lifespan, in all communities and with people with all healthcare needs. These definitions help to illuminate some of the central aspects of nursing care

# **Box 12.1: International Council of Nurses (ICN) Definitions of Nursing and a Nurse [1]**

*Definition of Nursing (Short Version)*

"Nursing encompasses autonomous and collaborative care of individuals of all ages, families, groups and communities, sick or well and in all settings. Nursing includes the promotion of health, prevention of illness, and the care of ill, disabled and dying people. Advocacy, promotion of a safe environment, research, participation in shaping health policy and in patient and health systems management, and education are also key nursing roles".

### *Definition of a Nurse*

"The nurse is a person who has completed a program of basic, generalized nursing education and is authorized by the appropriate regulatory authority to practice nursing in his/her country. Basic nursing education is a formally recognized program of study providing a broad and sound foundation in the behavioral, life, and nursing sciences for the general practice of nursing, for a leadership role, and for post-basic education for specialty or advanced nursing practice. The nurse is prepared and authorized (1) to engage in the general scope of nursing practice, including the promotion of health, prevention of illness, and care of physically ill, mentally ill, and disabled people of all ages and in all health care and other community settings; (2) to carry out health care teaching; (3) to participate fully as a member of the health care team; (4) to supervise and train nursing and health care auxiliaries; and (5) to be involved in research".

that include activities related to the scope of nursing; how care is given; what knowledge, skills and education are needed and how nursing constitutes a profession.

Nursing is both a caring art and a science and it encompasses a distinct body of knowledge, separate from that of medical or allied health professional colleagues. Knowledge is specific information about something and caring is behaviour that demonstrates compassion and respect for another, but these simplified concepts do not truly reflect the synthesis of both knowledge and the art of caring that makes orthogeriatric nursing unique [3].

# **12.3 The Orthogeriatric Patient**

Patients with fragility fractures present across the spectrum of health service providers including general practice, community services, acute care services including emergency portals, operating departments, inpatient and outpatient services and hospital- or community-based rehabilitation services. Care takes place in an environment that is largely not conducive to the care of frail older people. The provision of safe, effective care for hospitalised patients following hip fracture is particularly complex and demands a focus on achieving best outcomes for frail elderly hospitalised patients. This complexity is generated by multiple interlocking problems related to both breadth (range) and depth (severity) of healthcare need [4] related to three main characteristics; the person, the fracture and the care environment—all of which have a significant impact on patient care outcomes [5]. Orthogeriatric patients have usually fallen, often have multiple co-morbidities and are frequently frail. These problems interact in the aftermath of fragility fracture to increase care needs because of the increased physiological demands of the pre-, peri- and post-operative recovery and rehabilitation phases of care.

# **12.4 Care Quality**

Nursing is underpinned by a set of core personal and professional values, and the meaning of "quality care" varies depending on whether it is viewed from the perspective of the care giver or the care receiver but tends to be based on six core elements: a holistic approach, patient empowerment, professional accountability, patient safety, integrated teamwork and efficiency and effectiveness [6].

Compassion is a quality that enables nurses to be motivated to provide effective, person-centred care and is essential to quality nursing care; it includes empathy, respect and dignity, qualities that require understanding and recognition of another's suffering and a desire to do something about it. It is these qualities that enable nurses to see every patient as an individual and to humanise their care [7]. This is always important, but particularly when those receiving care are vulnerable as in the case of an elderly frail person who has suffered a hip fracture.

Quality care is significantly affected by communication, but even basic communication with vulnerable older adults accessing healthcare services is complex. Problems between individuals, families, caregivers and health professionals usually occur simply because communication is not effective. Everyone is a unique individual with personal values, beliefs, perceptions, culture and understanding of how the world operates. Fragility fractures predominantly, but not exclusively, affect older adults, who formed their opinions, values and beliefs in a very different society to that of the younger people who provide their care, and there can be a cultural age gap that leads to misunderstanding. Nurses need to understand the world from the perspective of an older person to communicate with them effectively and the sensory impairments and potential cognitive dysfunction that are common in older people add to the complexity of developing therapeutic communication. Understanding the acute needs of a patient with a fragility fracture and its management, as well as the chronic underlying diagnosis of osteoporosis and its treatment and impact, add to the already considerable complexity of being an older person with a set of co-morbid medical conditions and social and psychological intricacies resulting from primary and secondary ageing. Effective communication is two-way and involves ensuring that the messages are understood. Barriers to communication may need to be removed in simple ways such as ensuring glasses, hearing aids, interpreters and visual graphics are used to aid communication along with involvement of family friends, caregivers or advocates who know the patient best.

The success of healthcare delivery is often examined by measuring health status, outcomes, readmission rates, length of stay, complication rates and mortality [8], but these do not necessarily capture the specific contribution of nursing. Length of stay is a misleading measure for success as there are decreased levels of expert nursing care when patients are discharged or transferred too early [9]. Appropriate indicators of the quality of nursing care could include measures such as patient comfort and quality of life, safety outcomes (including healthcare-associated infection, pressure ulcers, falls and drug administration errors) and patient satisfaction [8]. In orthogeriatric care, nurse-sensitive indicators might be developed for; pain, delirium, pressure ulcers/injuries, hydration and nutrition, constipation, prevention of secondary infections and venous thromboembolism (VTE) [5].

# **12.5 The Unique Contribution of Nursing to Orthogeriatric Patient Outcomes**

Nursing care priorities are the fundamental aspects of nursing care including; comfort, hygiene, pain management, nutrition, hydration, remobilisation and rehabilitation. Evidenced-based nursing can coexist with medical models of care, reducing the risk of developing complications, aiming to reduce the risk of morbidity and mortality, whilst improving recovery, maintaining functional ability and improving patient outcomes and experiences [5].

Nurses often become specialists in specific aspects of healthcare to enable them to focus on providing skilled care based on up-to-date knowledge to a defined group, so they often work in teams with other specialists such as medical practitioners and therapists, collaborating in pathways and sharing evidence across professions. Orthogeriatrics was first used to describe collaboration between the specialties of orthopaedic surgery and geriatric medicine but has come to denote a multidisciplinary approach to the hospital care of patients with fragility fractures that recognises the complex specific needs of this group. The concept of orthogeriatrics recognises the need to understand the holistic healthcare needs of frail, elderly patients with multiple health problems at the same time as working towards the best outcomes following fragility fracture [10].

# **12.6 Health Improvement and Health Promotion**

The education of patients for health improvement is often the role of nurses because they are the largest group of healthcare providers, but their actual and potential contribution to the management of chronic disease is underappreciated. The reasons for the prevalence of chronic disease in communities are complex; although there is an enormous body of evidence confirming how it can be avoided and treated, prevalence continues to rise and outcomes remain poor. Many patients are often inadequately informed, do not take responsibility for their condition and do not comply with instructions. Management of osteoporosis as a chronic condition provides an example of this; it is difficult to manage as it is often silent and the treatment can be unpalatable; concordance with oral bisphosphonates is poor, particularly in those patients not managed by ongoing nurse coordinator intervention. Nurses working in all settings play a central role in educating and coaching patients and families towards behaviour change that can positively influence health and healthcare outcomes following fragility fracture. The success of health improvement initiatives is reliant on nurses building trust with patients and families and working with them towards improvements in bone health and other related health domains. Every fall and fracture is an opportunity to prevent the next fall or fracture by MDT collaboration to prevent further falls and manage osteoporosis [11]. Nurses must be empowered to use the time they spend giving care with patients and families to educate them about the cause of fragility fractures and involve patients in developing an individualised plan of care for bone health.

# **12.7 The Nursing Resource**

Providing high-quality nursing care is labour intensive and requires an educated and highly skilled workforce led by experienced leaders of care who can direct a team of nurses who are qualified to plan evidence-based care and manage its delivery alongside well-trained and supervised support workers. Because of an ageing workforce and lack of political will to adequately fund nursing, global availability of enough skilled nurses to provide this fundamental and advanced care is a constant source of concern. It has, however, been demonstrated that an increase in a nurses' workload by one patient, from eight to nine patients per qualified nurse, increases the likelihood of an inpatient dying within 30 days of admission by 7% [12], indicating "missed care" [13] because of difficulties for nurses in undertaking actions to prevent complications and subsequent death.

# **12.8 Education for Orthogeriatric Care**

Competence is a hallmark of professional practice [14] but cannot be achieved if nurses do not have the requisite knowledge and skills to deliver care effectively to specific groups of patients. The benefits of providing highly skilled and specialist nursing of fragility fracture patients by practitioners with advanced, specific education have not been explored, but the development of specialist orthogeriatric nursing education could have a positive impact on patient outcomes [9]. Orthogeriatric nursing is an emerging and highly specialised branch of adult nursing that requires skills in both the care of the older person and the care of the orthopaedic and trauma patient so that the practitioner can bring both sets of skills together and use them to provide expert care to patients following fragility fracture with complex care needs that cannot be met without a deep understanding of how both age and frailty, as well as skeletal fragility and injury, impact on the planning and implementation of care. This requires specialist practice that applies knowledge and skills brought together from both nursing disciplines and knowledge and skills from partner professional specialities. Unfortunately, nurses are not well prepared to look after orthogeriatric patients as they have usually worked in orthopaedic trauma units and been educated for the care of adults with musculoskeletal problems rather than specifically to work with frail older people with complex needs. This can lead to the more complex needs of older people not being met. There is, consequently, an important education and skills gap and, at present, there are limited education resources available to support professional development of specialist orthogeriatric nurses.

# **12.9 Summary of Main Points for Learning**

The global shortage of nurses is now chronic and, unless the nursing resource is protected and grows, the potential of nursing to impact on patient outcomes and quality of fragility fracture care for patients with fragility fractures will be unmet [9]. "Looking after hip fracture patients well is a lot cheaper than looking after them badly" [15] and without a significant nursing resource to do the "looking after well", care will never be cost effective, and chronic health problems will never be prevented. Nurses are central to the coordination, provision and monitoring of orthogeriatric care.

# **12.10 Suggested Further Study**

Write a written reflection on how your learning from this chapter, and the entire book, reflects your current practice.

# **12.11 How to Self-Assess Learning**

Share your thoughts about the reflection you have written with your manager, mentor or preceptor and identify the ways in which you can become a more effective orthogeriatric practitioner.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Index**

### **A**

Abbreviated mental test score (AMTS), 111 Activities of daily living (ADLs), 47, 129 Acute confusional state, 110 Acute delirium, 61 Advanced activities of daily living (AADLs), 47 Advanced healthcare directive (AHD), 141 Airway, 55 Alcohol, 6 Anti-resorption, 10

## **B**

Baseline health, assessment of, 110 Basic activities of daily living (BADLs), 47 Bisphosphonates, 10 Blanching, 87 Blaylock Risk Assessment Screening Score (BRASS), 131 Body mass index (BMI), 6 Bone biology, 2 bone mass, 1 Bone mineral density (BMD), 6–8 Breathing, 55

### **C**

Calcium, 97 Calorie supplementation, 18, 19 Caregiver burden, 140, 141 Care integration, 133 CGA, *see* Comprehensive geriatric assessment Circulation, preoperative care, 55 Cognition, 47

Cognitive syndromes, 109 Communication, 138 Competence, 152 Comprehensive geriatric assessment (CGA), 110 assessment team, 49–51 assessment tools, 48, 49 cognitive impairment, 41 concept, 42–45 dimensions, 46–48 malnutrition, 41 nursing assessment, 42 posthospital discharge, 49 pressure ulcers, 41 secondary prevention, 49 Confusion Assessment Method (CAM), 112, 113 Constipation fundamental nursing care, 61 prevention of, 77 stasis, complications of, 71, 72 Continuity of care, 132, 133 Coordination of care, 133

## **D**

Deep vein thrombosis (DVT), 71 Deficit Accumulation Index, 18 Dehydration, 95, 98 evidence-based interventions, 100, 101 hydration and, 101–104 prevalence, 98 risk factors, 102 screening and assessing patients, 99, 100 symptoms, 98

© The Editor(s) (if applicable) and the Author(s) 2018 155 K. Hertz, J. Santy-Tomlinson (eds.), *Fragility Fracture Nursing*, Perspectives in Nursing Management and Care for Older Adults, https://doi.org/10.1007/978-3-319-76681-2

Delirium

acute, 61 acute confusional state, 110 assessment, 110–112 consequences, 110 identifying delirium, 112 management, 113, 114 medication, 114, 115 predisposing and precipitating factors, 111 prevention, 112, 113 Dementia advocacy, 117, 118 capacity, 116, 117 early stage, 115 late stage, 115 middle stage, 115 two-stage assessment, for mental capacity, 117 Depression, 28, 118, 119 assessment, 119, 120 interventions, 120 Deprivation of Liberty Safeguard (DoLS), 118 Disability, preoperative care, 55 Discharge and post-hospital care, 129 admission phase, 131 continuity of care, 132 discharge home, 130 discharge phase, 131, 132 hospitalisation phase, 131 nurse's role in, 133 setting, 130, 131 Dual-energy X-ray absorptiometry (DXA), 7, 8, 10, 36 DVT, *see* Deep vein thrombosis

### **E**

Eating disorders, 6 Effective communication, 150 End of life care, 141–143 Ethnicity, 5 Exercise, 6, 69, 70 assessment, 74 evidence-based interventions, 74, 75 frailty, 18 sarcopenia, 21 Exposure, 55

## **F**

Falls, 29 assessment, 114 causes, 29 CGA, 47 extrinsic factors, 29

frailty, sarcopenia and, 23 holistic, person-centred assessment, 28 intrinsic factors, 29 prevention strategies, 30 environment, 30 exercise, 31 fear of falling, 31, 32 footwear and foot care, 31 medication review, 31 pathways and guidelines, 32 visual impairment, 31 screening and assessment, 29, 30 Family partnerships, 138, 139 assessment, 139, 140 caregiver burden, 140, 141 legal and ethical considerations, 141 Fear of falling, 31, 32 Femoral neck fracture, 56 Financial support, CGA, 47, 48 4AT, 111 Fracture Liaison Coordinator, 33, 34, 37 Fracture liaison services (FLS), 7, 28, 33, 34 Fracture Risk Assessment Tool (FRAX®), 1, 8, 36 Fragility fracture, 125, 144 and osteoporosis, 3, 4 case finding, 7 diagnosis, 7 risk evaluation, 7, 8 risk factors, 5–7 secondary osteoporosis, differential diagnosis of, 9 treatment, 9–11 secondary' prevention, 1 Frailty, 16 assessment, 17, 18 frailty index, 18 interventions, 18, 19 Functional Independence Measure (FIM®), 131 Functional status, 46, 47

## **G**

Genant classification, 8 Geriatric syndrome, 41

#### **H**

Healthcare-associated infection, 61 Health education, 37 Hip fracture, 4 diagnosis and surgery, 56–58 discharge and post-hospital care, 129 DVT, 71 intertrochanteric, 56 pain management, 58, 59

palliative and end of life care, 141, 142 perioperative care, 54 preoperative care, 54 rehabilitation, 126, 128 Hospital-acquired infections (HAIs), 71, 92 Hospital Anxiety and Depression Score (HADS), 119 Hospital Elder Life Program (HELP), 114 Hydration, 95, 101 assessment, 102, 103 evidence-based interventions, 103, 104 fundamental nursing care, 61 screening and assessing patients with dehydration, 102 Hypogonadism, primary/secondary, 5

# **I**

Incontinence-associated dermatitis (IAD), 86 Independent mental capacity advocate (IMCA), 117 Informal caregiving, 138 Instrumental/intermediate activities of daily living (IADLs), 47 Integrated care pathways (ICPs), 127 International Osteoporosis Foundation (IOF), 33 Intertrochanteric hip fracture, 56

#### **L**

Long-term glucocorticoid therapy, 5 Low energy trauma, 3

#### **M**

Malnutrition, 41, 95, 98 evidence-based interventions, 100, 101 hydration and, 101–104 prevalence, 98 risk factors, 102 screening and assessing patients, 99, 100 symptoms, 98 Mechanostat theory, 21 Menopause, 5 Mental Capacity Act, 116, 118 Mini Mental State Examination (MMSE), 112 Mobility, 68, 69 assessment of, 72, 73 evidence-based interventions, 73, 74 Modified falls efficacy scale, 30 Monoclonal antibody, 10 Montreal Cognitive Assessment (MoCA), 112 Mood, 47 Multidisciplinary team (MDT), 31, 73, 129, 144, 147, 151

Multimodal analgesia, 59

# **N**

Nutrition calcium and vitamin D, 97 fundamental nursing care, 61 healthy diet, for older adults, 96, 97 malnutrition and dehydration, 98 evidence-based interventions, 100, 101 hydration and, 101–104 prevalence, 98 risk factors, 102 screening and assessing patients, 99, 100 symptoms, 98 pressure injury prevention, 91 sarcopenia, 21

# **O**

Oestrogen deficiency, 6 Opioid analgesia, 59 Orthogeriatric nursing, 43, 147, 148 care quality, 150 caring art and a science, 149 contribution, 151 fundamental nursing care, 60, 61 health improvement and health promotion, 151 hip fracture and diagnosis and surgery, 56–58 pain management, 58, 59 International Council of Nurses (ICN) definitions of nursing, 148 orthogeriatric care, education for, 152 orthogeriatric patient, 149 perioperative care, 54 postoperative care, 60 preoperative care, 54, 55 preparation for discharge, 62 resource nursing, 152 Osteoblasts, 2 Osteoclasts, 2 Osteocytes, 2 Osteopenia, 27 Osteoporosis epidemiology, 3 fragility fracture and, 3, 4 case finding, 7 diagnosis, 7 risk evaluation, 7, 8 risk factors, 5–7 secondary osteoporosis, differential diagnosis of, 9 treatment, 9–11 normal and osteoporotic bone, 2, 3

#### **P**

Pain, 4, 48, 58–60, 68, 70, 75 Palliative care, 137, 141–143 Parathyroid hormone, 10 Patient-centred medicine, 138 Patient Health Questionnaire 9 (PHQ-9), 120 Peak bone mass, 2 Perioperative care, 54 Physical Frailty Phenotype (PFP), 17 Polypharmacy, 19, 47, 48 Preoperative care, 54, 55 Pressure injuries, 86 classification of, 87, 88 pathophysiology and causes, 86, 87 prevention, 88 evidence-based preventive interventions, 89–91 patient assessment, 89 Pressure injury prevention, 61 Previous fracture, 5 Primary/secondary hypogonadism, 5 Protein supplementation, 18, 19 Proximal femur fracture, 4 Public health, 3 Pulmonary infections, prevention of, 77 Pulmonary tract infection, 71

#### **Q**

Quality care, 150 Quality of life, 4

#### **R**

Rehabilitation mobility and exercise, 127, 128 pathways, 126, 127 patient motivation, 128, 129 primary outcomes, 126 Remobilisation evidence-based interventions, 73, 74 mobility and, 68, 69 potential, 72, 73 Remodelling, 2 Rheumatoid arthritis, 5

#### **S**

Sarcopenia, 19 clinical consequences, 21 interventions, 21–23 screening and assessment, 20 Secondary fracture prevention, 32, 33, 61 fracture prevention services and guidelines, 33–35 models of care, 28 morbidity, mortality and functional ability outcomes, 28 typical patient journey, 35 assessment, 36 evaluation, 38 FLS, first contact with people, 36 health education, 37 personal plan, establishing, 37 Selective oestrogen receptor modulators (SERM), 10 Smoking, 6 Social support, CGA, 47, 48 Stasis complications of, 70 assessment, 75, 76 constipation, 71, 72 DVT, 71 prevention, 76–79 pulmonary and urinary tract infection, 71

### **T**

Thirty-second chair stand, 30 Thromboembolism, stasis complications, 71, 76, 77 Timed Up and Go test (TUG), 30 Tinetti balance assessment tool, 30

### **U**

Urinary catheters, 71 Urinary tract infections (UTI), stasis complications, 71, 77

## **V**

Vertebral fracture, 4 Vertebral Fracture Assessment (VFA), 8 Vitamin D supplementation, 6, 19, 97

#### **W**

Weight loss, 19 Wound healing, 91 Wound management, 91, 92 preventing wound infection, 92, 93 studies, 93