Patent Abstract:
the invention relates to a system and a method for assessment of walking and miming gait in human . the method is preferably based on the fusion of a portable device featuring inertial sensors and several new dedicated signal processing algorithms : the detection of specific temporal events and parameters , 5 optimized fusion and de - drifted integration of inertial signals , automatic and online virtual alignment of sensors module , 3d foot kinematics estimation , a kinematic model for automatic online heel and toe position estimation , and finally the extraction of relevant and clinically meaning - full outcome parameters . advantageously including at least one wireless inertial module attached to foot , the system provides common spatio - temporal parameters , with the 10 advantage of being able to work in unconstrained condition such as during turning or running . it furthermore may provide original parameters for each gait cycle , both temporal and spatial , and their inter - cycles variability . the system and method according to the invention allows the assessment of various aspects of gait which have shown recently to be of premium importance in research and clinical field , including foot clearance , 15 turns , gait initiation and termination , running , or gait variability . the system may be light weight , easy to wear and use , and suitable for any application requiring objective and quantitative evaluation of gait without heavy laboratory settings .

Detailed Description:
the invention will be better understood below by way of non - limitative examples and the following figures : fig1 — s - sense module with compliant foam attached with velcro to hind part of shoe fig2 —“ physilog 3 ” foot - worn sensors featuring 6dimu and its fixation to user &# 39 ; s shoes on forefoot fig3 — the inertial signals and the temporal events for one typical gait cycle and corresponding events . inertial signals re scaled to be depicted together in one graph . fig5 — de - drifted integration of vertical acceleration ( a ) to obtain vertical velocity ( v ) using linear function versus p - chip interpolation function ( pif ) fig6 — 3d gait parameters estimation from 3d foot position ( p ) and azimuth ( θ ): stride length ( sl ), stride velocity ( sv ), foot clearance ( fc ) and turning angle ( ta ). fig8 — heel and toe clearance estimation obtained from inertial sensors and kinematic model fig9 — heel (*) and toe (*) trajectory estimated by foot worn sensor system during a typical gait cycle a wireless 6 dimensional - inertial measurement unit ( 6d - imu ) referred as “ s - sense ” has been designed ( van de molengraft et al ., 2009 ). s - sense module is a small ( 57 × 41 × 19 . 5 mm 3 ) and low power ( 18 . 5 ma @ 3 . 6v ) stand - alone unit integrating microcontroller , radio transmitter , memory , three - axis accelerometer ( adxl , analog device , range 3 g ), three - axis gyroscope ( adxrs , analog device , roll , yaw with 300 deg / s range , pitch with 800 deg / s range ), and batteries , and can also feature datalogger recording on flash memory cards such as sd - card . s - sense modules can be fixed on shoes at hind foot position using a compliant foam structure and double sided velcro straps ( fig1 ). raw sensor data was low - pass filtered at 17 hz , sampled on 12 bits at 200 hz , and wirelessly transmitted in real time to a pc using “ s - base ” receiver plugged in usb . in another embodiment of the invention , sample rate can be lowered to 100 hz and / or sampled with higher resolution . signals from two s - senses were synchronized by considering the absolute real time clock sent by the base station to each module at the start of recording . raw data were preliminary processed to extrapolate some missing data due to wireless data loss or sensor &# 39 ; s output saturation ( van de molengraft et al ., 2009 ). data from the two feet were finally converted to physical units ( g or ° / s ) using in - field calibration method ( ferraris et al ., 1995 ). more generally , the invention can use any equivalent sensor measuring 3d accelerations and 3d angular velocities on foot . for example , it can be a 6d - imu module directly integrated in shoe . another new wireless 3d gait analysis system was designed , the “ physilog 3 ” ( fig2 ), which is also small and lightweight and more conveniently worn on the front foot using shape - memory foam and velcro bands , and contain an equivalent 6d - imu . in addition , the module might contain or be synchronized with additional sensors such as gps receiver , force sensors , magnetometers , optical range sensors or emg electrodes , providing extrinsic information . during normal walking gait , stance phase is the period between initial contact , referred as heel - strike ( hs ), and terminal contact , referred as toe - off ( to ), with the ground . in addition , this period encapsulates the instant where toes touch the ground , referred as toe - strike ( ts ), and the instant where the heel leave the ground , referred as heel - off ( ho ). we call the successive events of hs , ts , ho , and to the “ inner - stance phase events ”. the two negative peaks on pitch angular velocity ( ω p ) are known to be approximate estimates of heel - strike and toe - off events ( aminian , 2002 ). those features have shown to be robust on a wide range of healthy and pathologic populations ( salarian , 2004 ) and were used to distinguish them ( aminian , 2004 ). then , we determined a time window between these two peaks to find other features to detect hs and to based on the norm of the accelerometer signal (∥ a ∥) and the norm of the gyroscope signal , i . e ., angular velocity , (∥ q ∥) where the euclidian norm of a vector x =[ x 1 , x 2 , x 3 ] is defined as ∥ x ∥=√( x 1 2 + x 2 2 + x 3 2 ). regarding the period between ts and ho events , it is characterized by a lower amount of movement since the ground applies a mechanical constraint to the foot , and it is so - called foot - flat period . consequently , ts and ho features are detected using a threshold on derivative of angular velocity norm (∥ q ∥′), on pitch angular velocity ( ω p ), and the absolute value of the jerk , indicated by the derivative of accelerometer signal &# 39 ; s norm (∥| a ∥′|). for all inertial signals , the use of the norm of 3d signals allows being independent of sensor placement on the foot , making it more repeatable and no specific calibration to align them with anatomical frames ( cappozzo , 1995 ) is required whereas the use of only pitch signal allow to use a single sensor configuration . those entire hypotheses for detecting temporal events are illustrated in fig3 . during running gait , foot kinematics can be slightly modified . other robust features and thresholds were adapted consequently to detect temporal events . furthermore , temporal event detection also provides the static periods where no movement is sensed ( typically when signal variations are bellow a defined threshold ), referred as motion - less for those which occurs during stance phase . finally , midswing event ( ms ) detected from positive peak of pitch angular velocity during swing provides a robust hypothesis for detecting gait cycle in any condition . based on the detected temporal events , meaningful metrics ( e . g . parameters ) for clinical gait analysis can be defined ( fig4 ). thereby , stance phase was defined as follow : where t ( ) is the occurrence instant of each event . subsequently , the three periods composing the stance phase were defined as follows : futhermore , swing time , gait cycle time , can also be calculated as follow : gait cycle = t ( hs + 1 )− t ( hs ) ( or t ( to + 1 )− t ( to ) etc . . . . ) in case the system is mounted on two feet , double support parameters can also be calculated using classical definition ( aminian , 2002 ). absolute metrics are calculated in milliseconds , and relative metrics are calculated as percentage of the stance time or gait cycle time . when foot - worn sensors are fixed on subject &# 39 ; s foot , their relative orientation in space is unknown . 3d virtual alignment method consists in finding the initial orientation of the sensor , represented equivalently by a 3 × 3 matrix or a quaternion or an axis angle . initial 3d orientation of module is obtained by using 3d acceleration ( a n ) as inclination during static periods ( provided by temporal events for example ), and azimuth was set at which maximized the variance of angular velocity signal around pitch axis of foot . this original method has the great advantage of not requiring any functional calibration or precise positioning of the sensor module on subjects foot . during each gait cycle n , 3d orientation ( r n ), velocity ( v n ), and trajectory ( p n ) of foot were estimated from inertial signals . practically , this involves the temporal detection of cycles , the knowledge of initial conditions of position and orientation , the gravity cancellation of measured acceleration , and the de - drifted integration of g - free acceleration . moreover , kinematics measured by sensors in xyz should be expressed in xyz to be compared with reference . fig5 illustrates the main algorithmic steps . initial conditions were updated for each cycle n at tff n , where the foot was considered motion - less . initial 3d orientation of sensor module ( r0 n ) was obtained by using 3d acceleration ( a n ) as inclination ( i . e . by aligning z axis with z ), and azimuth was set at the value derived from the orientation at last sample ( n ) of previous step ( r n - i ( n ))). it means the system can work with any terrain inclination , i . e . that the invention can also detect the initial conditions during walking in slopes , thus making it possible to analyze 3d foot kinematics in such situations . gravity cancellation was achieved by aligning the accelerometers &# 39 ; axes ( xyz ) with fixed frame ( xyz ) and subtracting gravity vector . from initial orientation r0 n , the orientation of the foot relative to fixed frame ( r n ( i )) was updated at each time frame ( i = 1 , 2 , . . . , n ) by a quaternion - based time integration of angular velocity vector ω n between two successive foot - flats ( tff n - i , tff n ) ( sabatini , 2005 ; favre et al ., 2008 ). at each time frame i of cycle n , using measured accelerations ( a n ( i )), gravity - free component of acceleration in fixed frame ( a n ( i )) can be summarized by ( 1 ). a n ( i )= a n ( i )* r n ( i )− g , where g =( 0 , 0 , 1 ) ( 1 ) single and double - integration of gravity - free acceleration ( a n ) allowed obtaining 3d velocity and position of foot at each gait cycle n . by assuming that foot velocity is null at each tff n ( curey et al ., 2004 ), estimation of velocity ( v n ) was obtained by trapezoidal integration of a n and position ( p n ) was finally deduced by simple trapezoidal integration of velocity ( v n ). integration step which is performed at 2 . 5 is prone to drifting errors , due to various sources such as electronic noise or sensors non - linear behaviors . so in practice , to obtain acceptable performance for estimating 3d foot kinematics , system drift needs to be corrected . this can be done using a classic linear de - drifting at each gait cycle between two motion - less period . in a preferred embodiment of the invention , the drift is removed by subtracting a sigmoid - like curve modeled based on a p - chip interpolation function ( carlson and fritsch , 1985 ). the p - chip interpolation function ( pif ), is defined between the value of a n - 1 ( tff n - i ) and a n ( tff n )), ( fig6 ). as it is illustrated in fig6 , it provides a better estimation of drift in the particular case of gait since it is proportional to the quantity of movement , thus allowing improvement of accuracy and precision of 3d foot kinematics . from the 3d foot kinematics , in addition to the overall 3d foot trajectory , the following gait parameters were extracted at each cycle n for both reference system and foot - worn sensors using ( 2 ), ( 3 ), ( 4 ) and ( 5 ), where n represent the last sample of cycle n : stride length ( sl ) was defined as the distance measured between two successive foot - flat positions of the foot . this calculation is valid for curved and turning path as well ( huxham et al ., 2006 ). foot clearance ( fc ) was defined as the maximal foot height during swing phase relative to the height at foot - flat : fc n = max ( p n ( 1 ), p n ( 2 ), . . . , p n ( n ))− p n ( 1 ) ( 3 ) stride velocity ( sv ) was considered as the mean value of foot velocity in ground plane ( xy ) during each gait cycle : sv n = mean ( v n | xy ( 1 ), v n | xy ( 2 ), . . . , v n | xy ( n )) ( 4 ) turning angle ( ta ) was defined as the relative change in azimuth ( i . e . the projection of orientation in ground plane ( xy )) between the beginning and the end of gait cycle . ta n = θ n ( n )− θ n ( 1 ) where θ n = r n | xy ( 5 ) extracted spatial parameters and turning are illustrated in fig7 . foot clearance provided by 2 . 7 gives general information which is dependent to sensor positioning on foot . typically , a bigger fc is measured if sensor is on the heel compare to the case where sensor is on the foot . in order to be independent of sensor positioning , we design a method to automatically model sensor relative position to heel and toe , based on 3d foot kinematics and biomechanical assumptions . the relative position of sensor module in foot sagittal frame to the toe and heel of the subject can be represented by 3 variables ( fig8 ). by combining the position of sensor ( p ), the knowledge of foot orientation ( r ) and shoe size and assuming that at toe - off ( to ) ( respectively heel - strike ( hs )), toe &# 39 ; s ( respectively heel &# 39 ; s ) vertical position is 0 , { a , b , c } during gait for each cycle n were computed by solving the following analytical equations : knowing sensor trajectory ( p sensor ) and orientation ( r ) and relative position to heel and toe ({ a , b , c }), heel clearance ( hc ) and toe clearance ( tc ) can be estimated by the following trigonometric relations : in addition to fc , parameters such as minimal toe clearance ( mtc ) can be extracted from heel and toe clearance at each gait cycle as illustrated in fig9 . other parameters such as maxhc , minhc , maxtc or other statistical measures can be extracted from clearance curves according to fig1 . since subjects are not always performing pure straight walking , direct variability of gait observed can be due to the turning at the end of the pathway . so in order to focus on the assessment of the ‘ intrinsic dynamics ’ of continuous , normal walking , we need to ensure that the analysis is not influenced by those atypical strides outliers . detection and correction of outliers in gait parameters series consists of the following steps : detect the gait cycles during turning , i . e . when ta is above a threshold obtained empirically replace turning gait cycle parameter with its median value during straight walking or simply remove it from the analysis . apply statistical method such as three - sigma rule to the new series in order to remove outlier related to other origins such as data loss or walking breaks ( facultative ) to further quantify the stride - to - stride fluctuations in walking , there are various tools including commonly used linear parameters as well as non - linear methods . parameters time series can be foot clearance , stride length , stride velocity , gait cycle time , or any other spatio - temporal parameter provided by the invention and previously described methods . in a given time series a data sample can be identified as a ‘ signal permutation / turn ’ ( note that ‘ turn ’ is not related to walking turn / outliers !) if it satisfies the following two criteria : 1 ) it represents an alteration of direction in the signal , i . e ., a change in the sign of the derivative and 2 ) the difference ( absolute value ) between its amplitude and that of the preceding sample should be greater than a specific threshold . the number of signal permutation / turns in a time series represents the degree of signal variability . the cv and related linear variability parameters quantify the magnitude of stride - to - stride variability but are not sensitive to changes in the ordering of the stride times or the dynamics randomly reordering a time series will not affect the magnitude of the variability but may dramatically alter the dynamic properties . to quantify how the dynamics fluctuate over time during the walk , fractal dfa analysis and symbolic entropy measures are applied to the stride time series . in a preferred embodiment of the invention all previously discussed parameters are taken into consideration for the 3d gait assessment . the system may provide objective evaluation of walking and running gait performance of a subject through original parameters such as foot clearance , foot - flat duration etc . . . . , in any sort of walking situation or test . the diagram of fig1 gives an overview of the interactions between the subcomponents of a system including all those parameters . the invention differs from the prior art in that it uses a least one original parameter ( either temporal or spatial ) that can be measured when performing any gait activity . it allows assessment during straight and curved trajectory , during outdoor locomotion , in ramp , stairs or even during running a new drift compensation method renders the system more robust for precise and accurate gait assessment despite errors due to the sensors . these extracted parameters show promising preliminary discriminative performance , as it was possible to distinguish young and elderly subjects . the system according to the present invention was used successfully in more than 600 elderly subjects . it may be used for various purposes such as clinical gait evaluation , performance assessment in athletes , functional tests in patient with gait impairments , treatment follow - up , etc . . . . for other application such as long - term tracking or clinical research however , it could require to be coupled with additional sensors such as magnetometers , gps , emg electrodes etc . . . . the method according to the invention can be applied with sensor worn on any foot position . compared to other inertial - based gait analysis system ( aminian et al ., 2002 ; salarian et al ., 2004 ; sabatini et al ., 2005 ; schepers et al ., 2007 ), similar or slightly better accuracy and precision was obtained for sl and sv . the method also provides stride - to - stride variability of gait , with the advantage of being able to extract outliers due to turning or other extrinsic variation that can be measured from the system . in controlled environments , previous studies showed significant associations between gait variability and various syndromes associated with aging , such as frailty ( seematter - bagnoud et al ., 2009 ), and fear of falling ( rochat et al ., 2010 ). the method according to the invention allows the analysis of curved trajectories , and provides new parameters such as ta and fc , which were not provided by any previous inertial - based system . actually , ta is an important outcome to evaluate gait in parkinson disease ( zampieri et al .) and fc , which was shown to be the most discriminative parameters between young and elderly subjects in our study , could also be an important new gait parameter to estimate risk of fall in elderly ( begg et al ., 2007 ; lai et al ., 2008 ). finally , the system according to the invention is lightweight and can be worn directly on user &# 39 ; s casual shoes or barefoot , thus minimizing intrusiveness and interference with normal gait conditions . it could also be directly integrated in the foot - wear . the system can be used as an objective tool in many applications requiring gait evaluation in real conditions without usual constraints of limited space due to laboratory settings . the invention is of course not limited to the examples discussed previously . aminian , k ., najafi , b ., bula , c . j ., leyvraz , p . f ., robert , p ., 2002 . spatio - temporal parameters of gait measured by an ambulatory system using miniature gyroscopes . journal of biomechanics 35 , 689 - 699 . aminian , k ., 2006 . monitoring human movement with body - fixed sensors and its clinical applications . invited chapter in “ computational intelligence for movement sciences : neural networks and other emerging techniques ”. idea group pub ., p . 101 - 138 . bamberg , s . j . m ., benbasat , a . y ., scarborough , d . m ., krebs , d . e ., paradiso , j . a ., 2008 . gait analysis using a shoe - integrated wireless sensor system . ieee transactions on information technology in biomedicine 12 , 413 - 423 . begg , r ., best , r ., dell &# 39 ; 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