the present invention consists of a system and method for performing the following tasks during the user &# 39 ; s everyday life : ( 1 ) monitoring the user &# 39 ; s physical activity ; ( 2 ) automatically detecting the user &# 39 ; s falls ; and ( 3 ) assessing the user &# 39 ; s risk of falling . the second and third tasks are based on the results obtained from the first . as shown by fig1 a , the system includes a sensing module (“ sm ”) 101 for sensing , filtering and analyzing the user &# 39 ; s 100 body movements . the sm 101 is positioned on the user &# 39 ; s 100 upper body ( typically , on the user &# 39 ; s chest or torso ), and is comprised of one to three accelerometers , each of which may be mono - axial or multi - axial . the only constraints on the accelerometer configuration are that ( 1 ) accelerations in three perpendicular directions must be measured ; and ( 2 ) the accelerometer ( s ) is ( are ) configured to record accelerations in the frontal ( f ), vertical ( v ) and lateral ( l ) directions , which directions are relative to the user 100 ( see fig1 a ). in this document , all acceleration quantities are expressed in units of g ( i . e ., as multiples or fractions of g ), where g is the gravitational constant equaling 9 . 81 m / s 2 : for example , by this convention an acceleration magnitude of 9 . 81 m / s 2 ( in si units ) will be expressed 1 . the sm 101 may also include a data - storage system for storing the measured accelerations . an optional on - board communications system provides the sm 101 the capability to transmit the collected data and / or analyzed signals through either wired or wireless links for storage and / or for further offline analysis . analysis of the measured acceleration signals may be carried out ( 1 ) entirely on - board the sm 101 , ( 2 ) partially on - board the sm 101 and partially at other location ( s ), or ( 3 ) entirely at other location ( s ). in case some or all of the analysis is ( are ) carried out on - board the sm 101 , a data processing circuit will be included on - board the sm to carry out the required computations according to software - based algorithms developed as part of the present invention . in case some or all of the analysis is carried at location ( s ) separate from the sm 101 , the required data processing circuits performing the analysis may be ordinary or special - purpose computers , and are integrated with software - based algorithms developed as part of the present invention . monitoring the user &# 39 ; s physical activity consists of monitoring and assessing the user &# 39 ; s postures , movements , trunk tilt , as well as fall - related task parameters . to this end , the system computes various parameters associated with the subject &# 39 ; s movement from the data recorded by the sm 101 . these parameters consist of : ( a ) the subject &# 39 ; s trunk tilt ( specified in degree , measuring the angle between the subject &# 39 ; s trunk axis , and the axis aligned with the gravitational force — see fig1 b ); ( b ) the type of the subject &# 39 ; s postural transitions ( pt ); ( c ) the time of the subject &# 39 ; s postural transitions ; ( d ) the duration of the subject &# 39 ; s postural transitions ; ( e ) the duration of the subject &# 39 ; s locomotion ; ( f ) characterization of the subject &# 39 ; s locomotion ( gait analysis ); and ( g ) the type of subject &# 39 ; s postures ( e . g ., sitting , standing , lying ). use of accelerometers in place of gyroscopes by the present invention allows for long - term autonomous operability of the system . the associated challenges introduced by this replacement , however , consist of processing the resulting noisy accelerometer signals during everyday living activities . i . identifying the types of postural transitions , and computing their durations and occurrences : the flowchart in fig2 and fig3 a - 3 f demonstrate the operation of the algorithms , developed as part of the present invention , used to continuously determine the type , time , and duration of the subject &# 39 ; s postural transitions ( in this case , si - st and st - si ) during everyday movements . the algorithms use the frontal and vertical accelerometer signals — a f ( t ) and a v ( t ) respectively in fig1 a — where their time - varying nature is explicitly shown by including the time variable t in the notation used for these signals . in implementing the algorithms , the time variable t is by necessity discrete . fig3 a shows an example of the acceleration patterns recorded by the vertical and frontal accelerometers from an elderly subject with a high risk of falling ( a v ( t ): gray line 301 ; a f ( t ): black line ). as identified on the plot , the pattern consists of a sit - to - stand ( si - st ) postural transition followed by a period of walking and turning , followed by another postural transition ( stand - to - sit ; st - si ). as shown in fig2 , the algorithm performs the following steps on the frontal accelerometer signal to determine the occurrence , duration and type of the postural transitions : 1 ) segmenting , followed by wavelet filtering ( box 201 in fig2 ) to remove signal artifacts induced by locomotion ( e . g ., walking , climbing or descending the stairs , etc . )— see also the white trace 305 in fig3 b , an example of the resulting filtered signal a f - filt ( t ); 2 ) locating the local maximum peaks ( denoted by a f - p 306 in fig3 b ) in the filtered signal a f - filt ( t ) 305 through a peak - detection algorithm — this step corresponds to box 202 in fig2 ; 3 ) for each postural transition , corresponding to a particular a f - p 306 , computing an initial estimate of the postural transition duration ( δt 1 ) by ( boxes 203 and 204 ): ( i ) determining whether a f - p 306 is greater than a pre - defined threshold th1 ; ( ii ) if yes , locating the local minima 307 in a f - filt ( t ) 305 , within a specified time window , that precede and follow the particular maximum peak a f - p 306 see fig3 b ; ( iii ) computing δt 1 310 as the duration of the resulting time interval i 1 separating the local minima computed above . the above steps suppress and remove signal artifacts , such as noisy peaks , associated with shocks or other locomotion activities . following the initial determination of the postural transition duration ( δt 1 ), the system computes a more accurate estimate of the postural transition duration , δt 2 , by applying additional filters to the frontal acceleration signal only within a time interval that is centered at i 1 , but that is typically 10 % to 30 % longer in duration than δt 1 310 . such filtering of the frontal acceleration signal significantly decreases the requisite calculation costs , therefore enabling real - time implementation of the algorithm . if the value δt1 310 surpasses a defined threshold , th 2 ( box 205 in fig2 ), the following steps are performed on the frontal accelerometer signal a f ( t ) only during a time interval that is centered at i 1 but that is typically 10 % to 30 % longer in duration : 1 ) as represented by box 206 in fig2 , low - pass filtering the af ( t ) signal during the time interval i 1 by a wavelet ; 2 ) as represented by box 207 in fig2 , locating the maximum peak ( a f - p2 309 ) in the resulting filtered signal a f - filt2 ( t ) 308 during time interval i 1 ( see fig3 c ); 3 ) within a specified time window , locating a local minimum in a f - filt2 ( t ) closest to , and preceding , the particular maximum peak a f - p2 ( box 207 in fig2 ); 4 ) within a specified time window , locating a local minimum in a f - filt2 ( t ) closest to , and following the same maximum peak ( box 207 in fig2 ); 5 ) computing δt 2 311 ( see fig3 c ) as the duration of the resulting time interval i 2 separating the local minima computed above ( box 207 in fig2 ); the time of the maximum peak a f - p2 represents the time of the postural transition , and the parameter δt 2 311 represents the estimate of the duration of the postural transition . for each postural transition , following the computation of its time of occurrence and its duration , the system uses the step - by - step algorithm below to identify its type ( e . g ., st - si or st - si ): 1 ) as represented by boxes 209 and 210 in fig2 , for each postural transition if δt 2 exceeds a predefined threshold th 3 , estimate the trunk tilt angle in the sagittal plane , 8 , using a low - pass filtering of the a f ( t ) signal during the corresponding time interval i 2 — since a f ( t ) consists of a θ - dependent gravitational component as well as a higher frequency , pure frontal - acceleration component , low - pass filtering removes the pure frontal - acceleration component , leading to a quantity proportional to the sin ( θ ); 2 ) estimate the time - varying inertial frontal and vertical accelerations a f - interial ( t ) and a v - interial ( t ) through the following coordinate transformation ( see box 211 in fig2 ): where , as mentioned before , the acceleration signal is expressed in units of g ( g represents the gravitational constant ( 9 . 81 m / s 2 ))— see also fig1 b for a free - body diagram showing the inertial acceleration components ; 3 ) in parallel , apply an adequate , cascaded low - pass filter to remove the artifacts from a v ( t ), where the low - pass filter functions as follows : ( i ) removal of the gravitational component of a v ( t ) 312 ( fig3 e ) using the following equations ( see also box 211 in fig2 ): ( ii ) low - pass filtering the resulting signal a v - filt ( t ) 313 , leading to a v - filt2 ( t ); and ( iii ) filtering this signal by a moving - average filter to obtain a v - filt 3 ( t ) ( see also box 212 in fig2 ); 4 ) as exemplified in fig3 e - 3 f , determine the local peaks in a v - filt3 ( t ) using a peak detection algorithm ( box 213 in fig2 ); the resulting positive and negative peaks — p max 315 and p min 316 , respectively — exceeding a predefined threshold th 4 , are identified ( boxes 214 and 215 in fig2 ); 5 ) classify the detected postural transition as sit - to - stand or stand - to - sit through the sequence by which p max and p min occur : e . g ., a p max followed by a p min identifies the postural transition as a sit - to - stand pattern ( box 316 in fig2 ; see also fig3 e - 3 f ); 6 ) apply a post - processing algorithm to prevent misclassification of postures and postural transitions : for each postural transition , the classification as st - si or si - st will be corrected based on the preceding and subsequent sequences of postural transitions . fig4 describes in flowchart form the software - based algorithm , developed as part of the invented system , to identify the subject &# 39 ; s walking periods and measure his or her gait parameters . using data recorded by the accelerometers , the algorithm can distinguish left and right gait steps , as well estimate the spatiotemporal gait parameters , e . g ., swing , stance , double support , and gait speed . 1 ) remove from consideration data during time periods associated with postural transitions and lying ( boxes 401 - 402 in fig4 ); 2 ) compute the time - varying norm ( i . e ., time - varying magnitude ) of the vertical and horizontal accelerometer signals as : a f ⁡ ( t ) = [ a v - inertial ⁡ ( t ) + 1 ] ⁢ sin ⁡ ( θ ⁡ ( t ) ) + a f - inertial ⁡ ( t ) ⁢ cos ⁡ ( θ ⁡ ( t ) ) ; a v ⁡ ( t ) = [ a v - inertial ⁡ ( t ) + 1 ] ⁢ cos ⁡ ( θ ⁡ ( t ) ) + a f - inertial ⁡ ( t ) ⁢ sin ⁡ ( θ ⁡ ( t ) ) ; a v - filt ⁡ ( t ) = [ a f ⁡ ( t ) ] 2 + [ a v ⁡ ( t ) ] 2 ; where θ ( t ) represents the time - varying trunk angle , and a v - inertial ( t ) and a f - inertia ( t ) represent the time - varying vertical and frontal acceleration components , respectively ; fig5 b shows the resulting waveform , a v - filt3 ( t ) 503 — see fig1 b for the free - body diagram leading to the above formulas ; these formulas allow for suppression of the movement artifacts derived from the rotations of the subject &# 39 ; s trunk ; 3 ) remove the gravitational component from the vertical acceleration signal in two steps : first , use formula stated in step ( 2 ) to compute av - filt3 ( t ) 503 ; second , as shown by box 403 in fig4 , band - pass filter the result , leading to a v - filt4 ( t ) 504 ( see fig5 c ); 4 ) as represented by box 404 in fig4 , identify gait steps as the peaks 505 ( see , fig5 c ) in the a v - filt4 ( t ) signal 504 ; 5 ) verify the sequence of the detected peaks according to pre - defined conditions for gait patterns ( box 405 in fig4 ); 6 ) distinguish left and right steps ( box 407 in fig4 ) using the signal a l ( t ) from the lateral accelerometer — specifically , ( i ) the subject &# 39 ; s lateral velocity v l ( t ) is computed by integrating a l ( t ) during the recognized walking periods ; ( ii ) the relationship between the locations of the positive and negative peaks in v l ( t ) with the identified peak in the filtered vertical acceleration signal , a v - filt4 ( t ) 504 , allows for left and right steps be distinguished . this algorithm , furthermore , enables both the recognition of undetected gait steps , and the removal of false detected steps . the system , through another algorithm , computes the times of heel - strike ( initial contact ) and toe - off ( final contact ) events using information extracted from the frontal and vertical acceleration signals — this step corresponds to box 408 in fig4 . specifically , the local minimum and maximum peaks in the frontal acceleration signal surrounding each identified vertical acceleration peak are used to identify heel - strike event and toe - off events . following a heel - strike event , the subject &# 39 ; s trunk continues to moves forward . as the toe - off event occurs , the trunk slows down , leading to a negative peak in the frontal accelerometer signal . although a heel - strike event can be estimated using the vertical acceleration signal , when an impact is identified , the positive peak of the frontal acceleration pattern offers a significantly lesser noisy source for identification of the heel - strike event . determination of these event times facilitates the measurement of the temporal parameters ( e . g ., stance , swing , double support , step time , gait cycle time , etc .) and other relevant information associated with the spatial parameters ( i . e . stride velocity , step length and stride length ). gait speed ( i . e ., stride velocity ) is computed ( box 410 in fig4 ) using information from the detected gait cycle and the amplitude of acceleration during the double support . the system distinguishes lying from sitting and standing by comparing the angle of the vertical accelerometer signal a v ( t ) to that of the gravitational component . while the vertical accelerometer measures almost zero during lying periods , its value is significantly greater during sitting and upright postures — in some cases the value is close to the gravitational constant . the system identifies both the sit / stand - to - lying ( si / st - l ) and the mirror opposite ( i . e ., l - si / st ) postural transitions using the following algorithm : 1 ) band - pass filter the vertical accelerometer signal ( box 600 in fig6 ); 2 ) calculate the gradient of the resulting the filtered signal a v - filt5 ( t ) ( box 601 in fig6 ); 3 ) determine the maximum or minimum peak ( p v ) of this gradient ( box fig6 , box 602 ); 4 ) if the absolute value of the detected peak pv exceeds a pre - defined threshold th 5 ( box 603 , fig6 ), estimate the duration of lying postural transition using a local peak detection scheme to identify peaks preceding ( l initial ) and following ( l terminal ) p ∇ ( box 604 , fig6 ); 5 ) identify a lying posture at the time of the detected peak when ( i ) the absolute value of the detected peak exceeds a threshold th 5 ( box 603 , fig6 ); and ( ii ) the average value of a v - filt5 ( t ) during the 3 seconds preceding the l initial is i higher ( iii ) than a pre - defined threshold th 6 ( boxes 605 - 606 , fig6 ); and ) the average value of a v - filt5 ( t ) during the 3 seconds following the l terminal is lower than a threshold th 7 ( boxes 605 - 606 , fig6 ); 6 ) detect / identify a lying - to - sit / stand ( l - si / st ) postural transition at the time of the detected peak p v when ( i ) the absolute value of the detected peak exceeds a predefined threshold th 5 ( box 603 , fig6 ); and ( ii ) the average value of a v - filt ( t ) during the 3 seconds preceding the l initial is i lower than th 8 ( boxes 605 - 607 , fig6 ); and ( iii ) the average value of a v - filt5 ( t ) during the 3 seconds following the l terminal is higher than a threshold th 9 ( boxes 605 - 607 , fig6 ); 7 ) classify the lying posture further as lying on back , lying on the front , or on the sides ( left or right ) on the basis of the value of the frontal accelerometer signal ( box 608 , fig6 ); 8 ) further classify lying on the side into lying on the right and lying on the left according to the value of the lateral accelerometer signal . b . computing the risk of falling and the quality of the subject &# 39 ; s physical activity by monitoring the subject &# 39 ; s physical activity , the invented system both evaluates the quality of the subject &# 39 ; s physical activity , and computes the decline or progress in the subject &# 39 ; s functional performance . fig7 presents the flowchart of the corresponding software - based algorithm , developed as part of the invented system . the subject &# 39 ; s risk of falling ( rof ) during everyday life is computed by first quantifying the quality of the subject &# 39 ; s postural transitions . in turn , the quality of the postural transitions is quantified using the following algorithm : 1 ) estimate the lateral sway ( σ sway ) of the subject during pt by computing the standard deviation of the lateral accelerometer during pt ( box 700 , fig7 ); 2 ) estimate the jerkiness in the subject &# 39 ; s movement in all directions ( σ v - jerk , σ f - jerk , and σ l - jerk )— computed as the standard deviation of the band - pass filtered acceleration signals in the frontal , vertical and lateral directions ( box 701 , fig7 ); 3 ) compute the mean ( μ td ) and standard deviation ( σ td ) of the durations of the subject &# 39 ; s postural transitions ( δt 2 ), over a day ( box 702 , fig7 ); 4 ) compute the number of successive postural transitions ( n succ — pt ) required for a subject to accomplish a single task — an example is multiple unsuccessful attempts by a subject to rise from a chair ( box 703 , fig7 ); 5 ) evaluate the quality of physical activity by computing the fraction of the time that subject has active posture ( including walking ); the number of pts per day ; the number of walking episodes during a day ; and the longest continuous walking period per day ( boxes 704 - 706 , fig7 ); 6 ) evaluate the subject &# 39 ; s risk of falling by inputting the above parameters to a statistical model ( e . g ., stepwise ) that provides a linear combination of the calculated parameters to yield a single score representative the subject &# 39 ; s rof ( box 707 , fig7 ). a subject is considered to be at a high - risk of falling if the linear combination passes beyond a threshold , which may be predefined , or may change adaptively . to identify a subject at a high risk of falling more accurately , the system continually adjusts the requisite threshold values based on the history of falls or other similar events detected by the algorithm ( e . g ., high - impact experienced shortly after a postural transition , very short st - si durations , etc .) the present invention uses a novel algorithm , based solely on accelerometer signals , to automatically identify falls during the subject &# 39 ; s everyday life with high sensitivity and specificity . the fall - detection algorithm described here uses information about the subject &# 39 ; s physical activity , as well as posture . the flowchart in fig8 describes in complete the algorithm developed to automatically detect the subject &# 39 ; s falls . the following summarizes the algorithm : 1 ) compute the norm ( magnitude ) of acceleration in the transversal plane , a trans ( t ) from the frontal and lateral acceleration signals — a f ( t ) and a l ( t ), respectively — through : a trans ( t )=√{ square root over ([ a f ( t )] 2 +[ a v ( t )] 2 )}{ square root over ([ a f ( t )] 2 +[ a v ( t )] 2 )} ( box 800 ); 2 ) apply a peak - detection algorithm ( box 801 ) to atrans ( t ) to identify the presence of “ shocks ” a trans - pmax ; 3 ) confirm a fall event by considering the subject &# 39 ; s pa and posture prior to impact times ( marked by the identified shocks )— this step is carried out using algorithms described above ; 4 ) use different algorithms to identify a fall event , depending on the results of step ( 3 ) supra : ( i ) if impacts occur while subject is walking or turning , depending on whether the impacts occurred after right or left step , the algorithm chooses appropriate thresholds and coefficients required for subsequent steps ( th 8 : box 812 ; th 9 : box 814 ; and coefficients of the multivariable model : box 816 ); ( ii ) if activity preceding the shock is not identified as walking , turning or any sequential locomotion ( e . g ., walking upstairs or downstairs ,) the algorithm would identify as fall events only the shocks that occur after a postural transition to sitting or lying . ( iii ) next , thresholds and coefficients required for subsequent steps are modified ; 5 ) segment the shock - pattern following a postural transition into pre - shock , impact , and post - shock phases based on the location of local minimum peaks relative to the absolute maximum peak ( p max ) in the signal a trans ( t ) ( box 810 , trans , fig8 ); the set of thresholds chosen according to step ( 4 ) supra , and used by the algorithm depends on whether the post - shock posture is sitting or lying . 6 ) estimate the shock width ( δ shock ) using the local minimum peaks before and after each the peak p max ( box 811 , fig8 ); consider the peak to be an artifact and subsequently ignored if its width does not exceed the threshold th 8 ( box 812 , fig8 ); 7 ) if the peak is not an artifact , compute the subject &# 39 ; s speed during the pre - shock phase by integrating the pattern of vertical accelerometer — v v ( t ) ( box 813 , fig8 ); for the peak to be recognized as a fall , the peak of the velocity profile must exceed the threshold th 9 ( box 814 , fig8 ); 8 ) compute the following descriptors ( box 815 , fig8 ): ( i ) sum of all accelerations at the time of impact t impact as : ( iii ) the difference of speed in each direction at the impact time ( v f - impact , v v - impact , and v l - impact ); and ( iv ) energy of the norm of vertical and frontal acceleration during the impact phase ( δ shock ): 9 ) identify a fall event through a multivariable model ( stepwise or linear combination ) that uses the above descriptors as inputs and coefficients chosen in step ( 4 ) supra ( box 816 , fig8 ); 10 ) identify a fall as “ serious ” if the post - fall activities represent an unusual activity pattern , such as a long - duration rest , or multiple unsuccessful postural transitions ( boxes 818 - 819 , fig8 ); in one embodiment of the invention , an alarm will be set off following a “ serious ” fall ; the algorithms described above will classify the subject &# 39 ; s physical activity and posture , determine his or her risk of falling and quality of movements . in addition , several rules will be applied to improve the classifications performed by the above algorithms . these rules include , but are not limited to , the following : 1 ) if two contradictory states are detected ( e . g ., lying with walking or sitting with walking ) preference is first given to lying , then to walking , and finally to postural transitions . this rule is based on the rationale that the lying posture is classified with the least amount of error . it should be noted that since the algorithms for different postural detections operate independently , two contradictory sets of activities may be identified . 2 ) two successive postural transitions classified as the same type ( e . g ., si - st followed by si - st ) are not possible — the classifications are modified according to the preceding and subsequent activities . 3 ) elderly subjects cannot lean backwards after a si - st transition with a high likelihood . the algorithm estimates the trunk lean angle based on the trunk angle before ( θ pt - pre ) and / or following ( θ pt - post ) the postural transition . ( i ) both θ pt - pre and θ pt - post are estimated based on the mean ( e [.]) of the frontal acceleration during the rest period immediately before , or after a postural transition , according to the following formulas : where e [ a f ( t ) pre − pt − rest ] denotes the mean of the frontal acceleration signal during the rest period immediately before the postural transition ; e [ af ( t ) post − pt − rest ] denotes the corresponding mean after the postural transition . ( ii ) if the standard deviation of both frontal and vertical accelerations during a local interval before or after a postural transition were lower than a pre - defined threshold , the algorithm will classify that duration as a rest period . ( iii ) sensor inclination ( θ initial ) is computed from the average of the frontal accelerometer signal during a recognized walking episode containing at least ten steps : θ initial = sin − 1 ( e [ a f ( t )| walking ; 10 steps ]. ( iv ) the backwards - leaning state is detected if , subtracting θ initial from θ pt - pre ( or θ pt - post ) yields a value lower than a pre - defined threshold . 4 ) the duration of the lying posture should be more than a specified length ( e . g ., 30 seconds ). 5 ) for an episode to be classified as “ walking ,” it must include at least three successive steps within a predefined interval . 6 ) since it is improbable for a person , especially an elderly subject , to stand for long periods without any movements , long standing periods without additional activity ( e . g ., more than three minutes ) are interpreted as sitting . this rule applies if the standard deviations of both the vertical and frontal accelerations are below pre - defined thresholds . b . najafi and k . aminian , “ body movement monitoring system for elderly people , determines time and duration of postural transition ( 2000 , european and us patent ),” ep1195139 - a1 ep810920 5 oct . 2000 ; us2004015103 - a1 us398462 4 apr . 2003 , 2000 . b . najafi , k . aminian , f . loew , y . blanc , and p . a . robert , “ measurement of stand - sit and sit - stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly ,” ieee transactions on biomedical engineering , vol . 49 , pp . 843 - 851 , 2002 . b . najafi , k . aminian , a . paraschiv - ionescu , f . loew , c . j . bula , and p . robert , “ ambulatory system for human motion analysis using a kinematic sensor : monitoring of daily physical activity in the elderly ,” ieee transactions on biomedical engineering , vol . 50 , pp . 711 - 723 , 2003 . r . w . bohannon , a . w . andrews , and m . w . thomas , “ walking speed : reference values and correlates for older adults ,” j orthop sports phys ther , vol . 24 , pp . 86 - 90 , 1996 . k . aminian , b . najafi , c . bula , p . f . leyvraz , and p . robert , “ spatio - temporal parameters of gait measured by an ambulatory system using miniature gyroscopes ,” journal of biomechanics , vol . 35 , pp . 689 - 699 , 2002 . k . aminian , b . najafi , j . gramiger , p . morel , and n . bijan , “ autonomous measuring unit for human movement has sensors , conditioning circuit , display , and circuit for recording kinematic parameters of body segment ,” ecole polytechnique federale lausanne ( ecol - non - standard ) aminian k ( amin - individual ) bijan n ( bija - individual ) gramiger j ( gram - individual ) morel p ( more - individual ). k . aminian , k . rezakhanlou , e . de andres , c . fritsch , p . f . leyvraz , and p . robert , “ temporal feature estimation during walking using miniature accelerometers : an analysis of gait improvement after hip arthroplasty ,” medical & amp ; biological engineering & amp ; computing , vol . 37 , pp . 686 - 691 , 1999 . s . r . cummings , m . c . nevitt , and s . kidd , “ forgetting falls . the limited accuracy of recall of falls in the elderly ,” j am geriatr soc , vol . 36 , pp . 613 - 6 , 1988 . d . oliver , m . britton , p . seed , f . c . martin , and a . h . hopper , “ development and evaluation of evidence based risk assessment tool ( stratify ) to predict which elderly inpatients will fall : case - control and cohort studies ,” bmj , vol . 315 , pp . 104953 , 1997 . m . e . tinetti , t . f . williams , and r . mayewski , “ fall risk index for elderly patients based on number of chronic disabilities ,” am j med , vol . 80 , pp . 429 - 34 , 1986 . k . doughty , r . lewis , and a . mcintosh , “ the design of a practical and reliable fall detector for community and institutional telecare ,” j telemed telecare , vol . 6 suppl 1 , pp . s150 - 4 , 2000 . u . lindemann , a . hock , m . stuber , w . keck , and c . becker , “ evaluation of a fall detector based on accelerometers : a pilot study ,” med biol eng comput , vol . 43 , pp . 548 - 51 , 2005 . y . depeursinge , j . krauss , and m . el - khoury , “ device for monitoring the activity of a person and / or detecting a fall , u . s . pat . no . 6 , 201 , 476 ,” 2001 . n . noury , g . barralon , g . virone , p . boissy , m . hamel , and p . rumeau , “ a smart sensor based on rules and its evaluation in daily routines ,” presented at 25th annual international conference of the ieee eng . med . biol . society , 2003 .