Chapter 6. Gait cycle parameters recognition using the. wearable motion detector

Transcription

1 Authors:Che-Chang Yang ( ); recoended: Yeh-Liang Hsu ( ). Note: This article is the Chapter 6 of Che-Chang Yang s doctoral thesis Developent of a Hoe Telehealth Syste for Teleonitoring Physical Activity and Mobility of the Elderly. Chapter 6. Gait cycle paraeters recognition using the wearable otion detector This chapter presents the use of the wearable otion detector described in the previous chapter and the autocorrelation procedure to recognize gait cycle paraeters of Parkinson s disease (PD) patients in real-tie. The principles of the autocorrelation procedure dealing with acceleroetry data are introduced. The gait cycle paraeters derived fro the easured acceleroetry data of 5 elderly PD patients and 5 young healthy subjects are copared. The deterinable and discriinative characteristics of the selected gait cycle paraeters are highlighted. The possibility of developing a wearable syste utilizing autocorrelation procedure to recognize abnoral gaits, such as shuffling, festinating is discussed. 6.1 Gait dynaics and easureents for Parkinson s disease patients Gait dynaics reflect one s obility which can be affected by physical ipairent, age progress and changes in health status. Gait paraeters extracted fro coplex abulation dynaics can be iportant easures to assess functional ability, balance control and to predict risk of falling. Individuals with Parkinson s disease (PD) suffer progressive otor ipairents which can be characterized by resting treor, bradykinesia, rigidity, and postural instability. PD results fro deficiency of dopaine production due to the degenerative disorder of neurological functions. Levodopa (L-dopa)/Carbidopa treatent is the current therapy to reduce the PD syptos. The Unified Parkinson s Disease Rating Scale (UPDRS) [Fahn et al., 1987] and Hoehn and Yahr (H&Y) Modified Scale [Hoehn et al., 1967; Goetz et al., 2004] are the two ajor clinical easures to assess 1

2 the stages of PD advances. In addition, the tied up-and-go test (TUG) [Podsiadlo et al, 1991] and the Berg Balance Scale (BBS) [Berg et al., 1989] are also the two assessent tools in ters of obility. PD affects gait disorders in the lower extreities, such as reduced walking speed with increased cadence, reduced step-length, and increased stride-to-stride variability [Lowry et al., 2008]. Shuffling gait is also coonly observed fro oderate PD patients. The advanced PD patients ay have experienced the episodic gait disorders, such as festination, hesitation and freeze of gait (FOG) that occur occasionally and interittently and ay lead to falling and adverse health outcoes (e.g., hip fracture) [Hausdorff, 2009]. Gait evaluation is frequently based on observational interpretation that ay, however, vary aong clinicians or investigators. As a consequence, onitoring and analysis techniques for quantitatively investigating the Parkinsonian and pathological gaits have been widely developed and studied to provide objective easures. Gait dynaics can be accurately easured by using the optical otion capture systes [Melo Roiz et al., 2010]. As shown in Figure 6.1, these optical systes use high-speed infrared caeras to record the three-diensional positions of retro-reflective arkers attached to the joints and segents of the huan body during otion. The spatial-teporal gait paraeters including velocity, stride length, cycle tie and stance tie can be identified fro the kineatics data. 2

3 Figure 6.1 Typical attachent of the retro-reflective arkers [de Melo Roiz et al., 2010] The gait detection techniques utilizing pressure sensors ebedded in an overground walkway [Menz et al., 2004] or portable in-shoe pressure easureent syste have also been used [Feery et al., 2004]. These techniques detect foot contact (heel strike and toe-off) and even the foot pressure distribution to investigate spatial-teporal gait paraeters. The walking speed, cadence, right/left step lengths, and even the toe-in/out angles can be captured. Both gait onitoring approaches using optical techniques or pressure sensing techniques have to be liited in clinical/laboratory settings due to their expensive instruents, sophisticated syste setup and the needs of specialized personnel. Acceleroetry easureent using wearable systes has drawn a vast aount of research interests in the study of huan oveent. It is only recently that a few nubers of studies have reported gait analysis using acceleroeters while the acceleroeter-based trials in oveent classification, energy expenditure and fall detection have been largely studied [Mathie et al., 2004, Yang et al., 2010]. Though the Parkinsonian gaits have been well studied and described, only a few studies have investigated recognizing abnoral gaits based on wearable systes. A shank-ounted acceleroeter was used to onitor the freezing of gait of the PD patients [Moore et al., 2008]. A frequency spectra analysis was used to copute the frequency coponents of gait data. A freeze index is defined as the ratio of two spectral bands of different frequency coponents 0.5-3Hz and 3-8Hz. However, the power spectral analysis cannot be perfored in real-tie on the copact 3

4 wearable systes. A wearable syste using ARM7 processor was also deonstrated to detect FOG in real-tie fro every collected 0.32s acceleration data [Jovanov et al., 2009]. Due to the coputation constraints, it was reported that a longer saple data will produce longer latency of the syste which ight not be acceptable for a practical scenario. This chapter presents the use of the wearable otion detector described in the previous chapter and the autocorrelation procedure to recognize gait cycle paraeters of Parkinson s disease (PD) patients in real-tie. This chapter first deonstrates the extraction of gait cycle paraeters fro trunk acceleroetry easureent. Several gait cycle paraeters, including cadence, step regularity, stride regularly and step syetry, can be derived fro the acceleration signals in real-tie using the autocorrelation procedure which can be ipleented in icroprocessor-based devices of liited coputation capacity. Five PD patients and 5 young healthy subjects were recruited in a data collection session. The trunk accelerations during their guided walks were recorded using the wearable otion detector described in the previous chapter. The differences in the gait cycle paraeters between PD patients and the healthy subjects were copared and discussed. The study in this chapter can lead to a future developent of a wearable syste for recognizing PD-related abnoral gaits, such as shuffling, festinating, or freeze of gait and falls in real-tie, which can be iportant and beneficial in PD abulation rehabilitation and personal tele-care applications. 6.2 Signal processing for extracting gait cycle paraeters This section introduces the autocorrelation procedure to copute teporal gait cycle paraeters fro acceleration signals easured by the wearable otion detector. The ethod shows different gait characteristics coputed fro the exaple of different walking patterns in ters of the cadence, step regularity, stride regularity, step syetry Fundaentals of autocorrelation Autocorrelation have widely been used in any engineering and scientific fields. Based on the Pearson product oent correlation, autocorrelation is a nuerical ethod to calculate correlation of a signal with itself. It can be useful for analyzing spatial and teporal relationships between tie-varying signals such as physiological behaviors and huan oveents [Nelson-Wong et al, 2009]. The use of autocorrelation in huan oveent research is not as coon as in the other application fields [Moe-Nilssen et al., 2004; Keenan et al., 2005; Yang et al., 2010]. Autocorrelation is a ethod to estiate the repeating characteristics over a signal sequence containing periodic patterns and irregular noises. Consider a tie-discrete signal 4

5 sequence containing N signal points [ x 1, x 2, x 3,, autocorrelation coefficient another signal i x N ], the Equation (6-1) calculates the a, which is the su of the products of x i ultiplied by x at the given phase shift. The phase shift can be either positive or negative integers which range fro 0 to N 1, or fro 0 to1 N. a N xi i 1 x i (6-1) An autocorrelation sequence A can thus be represented by a series of autocorrelation coefficients a obtained at every phase shift. A raw signal sequence containing a periodic pattern can produce an autocorrelation sequence with its coefficient peaks where the phase shifts are equivalent to the periodicity of the raw signal sequence. Because the phase shifts can be either positive or negative fro zero, the pattern of autocorrelation sequence [ a, a 1,, a 0, a 1,, a 1, a ] fro the phase shift to can be syetrical with zero phase shift located centrally (=0). The autocorrelation sequence can either be biased or unbiased. The biased autocorrelation sequence coputed by Equation (6-2) that all the autocorrelation coefficients A biased can be a are divided by the nuber the of raw signal sequence N. Equation (6-3) generates the unbiased autocorrelation A unbiased by dividing the autocorrelation coefficient a by the nuber N. Figure 6.1 depicts the biased and unbiased autocorrelation sequences obtained fro a raw acceleration signal easured fro a young subject. Note that the nuber of autocorrelation coefficients equals 2N 1 when the phase shift ranges fro negative to positive. a biased 1 N N xi i 1 x i (6-2) a unbiased 1 N N xi i 1 x i (6-3) 5

6 Figure 6.1 The raw acceleration signal (above) and its biased (iddle) and unbiased (below) autocorrelation sequences noralized to 1.0 at zero phase shifts In Figure 6.1, both the biased and unbiased autocorrelation sequences differ very little at the center (zero th phase shift) and the neighboring coefficients next to the center. However the signals differ obviously other than the central part. When the phase shift increases, the nuber of the products x sued in its autocorrelation coefficient i x i a is N, and the value of a is saller. Therefore, the values of the a will attenuate in the biased pattern because every autocorrelation coefficients are divided by a constant nuber N. The autocorrelation coefficients are only divided by N in an unbiased pattern and therefore they will not attenuate obviously until the pattern deteriorates at the both end edges 1. The unbiased ethod is preferred because the biased ethod generates noticeable attenuation of coefficient values next to the zero phase shift fro a liited nuber of data. 1 MATLAB offers two autocorrelation functions: xcorr and xcov. The function xcov reoves the average of the data sequence before calling xcorr. This is suggested by Moe-Nilssen et al.[1] that this ethod rejects signal offset and would be atheatically suitable for algorith in use. 6

7 6.2.2 Interpreting gait cycle paraeters fro autocorrelation coefficient sequence Figure 6.2 shows an exaple of unbiased autocorrelation pattern segent coputed fro the vertical acceleration sequence (easured fro the waist, right iliu) during walking. Because the entire pattern is syetrical with the zero phase shift, only the right half part (i.e., the phase shift fro =0 to = N 1) of entire pattern noralized to 1.0 at its zero phase shift is considered. The coefficient at the zero th phase shift always has axial aplitude over the entire autocorrelation sequence because this zero phase shift point indicates the coparison of the original signal sequence to itself. By increasing the phase shift, the first coefficient peak D1 next to the zero phase shift point can be identified. The length n between the zero th phase shift point and D 1 iplies that the original signal sequence has ost apparent cyclic pattern with two signal points of n-sapled spaced. The phase shift n is the duration of the first doinant period which corresponds to one step. Siilarly, the second peaks D 2 indicates the second doinant period of the original signal sequence and it corresponds to a stride [Moe-Nilssen et al., 2004]. Zero phase shift D 1 D 2 n Saple Figure 6.2 The exaple of an autocorrelation sequence coputed fro the vertical acceleration easured at waist during walking (1) Cadence estiation Cadence is defined as the nuber of steps taken per inute. The duration of doinant period n can be used to derive estiated cadence. Assue M the nuber of steps per distance D walked, and V the walking speed (the distance D divided by the tie spent). Therefore, the cadence c 60MV. Let N be the nuber of signal saples per distance D 7

8 and f the sapling frequency such that Chapter 6. Gait cycle paraeters recognition using the wearable otion detector N M and n V f N. The cadence can be N f f substituted as: c 60MV In other words, the nuber n is the single n N n variable to estiate cadence as the sapling frequency is invariable. Figure 6.3 shows the exaple of the autocorrelation patterns of gaits at noral walking speed (above) and festinating speed (below) fro the sae person. In this figure the noral gait speed produces the cadence of 103 (n=29) and the festinating gait cadence is 188 (n=16). Figure 6.3 Autocorrelation patterns of gaits at noral walking speed (above) and festinating speed (below) of the sae person (2) Gait regularity and syetry As entioned previously, the first doinant period represents one step and the coefficient aplitude D 1 can be regarded as the step regularity. Siilarly, the coefficient aplitude D 2 is the stride regularity. If the value is ore close to 1.0 as its zero th phase shift, the step or stride repeats ore regularly fro the signal sequence. Note that the right or left steps cannot be distinguished according to the autocorrelation pattern. However, the ratio D D 1 2 which represents the syetry is defined and it is still valid [Moe-Nilssen et al., 2004]. Figure 6.4 shows the autocorrelation patterns obtained fro noral gait (above) and pretended crippled gait (below) perfored by a young adult. The step/stride regularity D 1 8

9 and D 2 of the noral gait are and which results in the gait syetry of On the contrary, the crippled gait shows D 1 = and D 2 = such that its gait syetry is The gait characteristics can be quantified and obviously the crippled gait shows lower gait syetry even though both the cadences reain siilar (103 and 100). Note that Figure 6.3 shows different cadences, however, both autocorrelation patterns show little difference in the step/stride regularity and syetry (noral: D 1 =0.7285, D 2 =0.8342, syetry=0.8732; festinating: D 1 =0.7130, D 2 =0.8871, syetry=0.8037)) because both gaits were easured fro the sae person. Figure 6.4 Autocorrelation patterns of noral gait (above) and pretended crippled gait (below) 6.3 Investigation of gait cycle paraeters between healthy subjects and PD patients In order to copare the gait cycle paraeters between healthy subjects and PD patients, 10 subjects were recruited in a gait data collection in this study. Five subjects are healthy young ales (26±3.1 yr) and without gait abnoralities, and the other 5 subjects (4 ale and 1 feale, 78±9.8 yr) are oderate PD patients diagnosed as Hoehn & Yahr (H&Y) stage II to III. For the PD participants, the test was conducted during their on-phase of the PD patients who had edication in the orning before the test. This data collection was approved by the Institutional Review Board at the Far-Eastern Meorial Hospital, Taipei. The participants were provided with necessary inforation about the test and they gave their infored consent before the test. 9

10 During the test, the participants wore the wearable otion detector and perfored the Tied-Up and Go (TUG) test and 5-eter walk on a level ground. The accelerations of the test oveents were recorded at a sapling rate of 50Hz by the wearable syste and a caera beside the walkway recorded the synchronized video of the test oveents for visual inspection of gaits. Each subject perfored three TUG tests, three 5-eter walks at noral walking speed, and another three 5-eter walks at fast walking speed. The noral walking speed is self-regulated by the subjects, and is subject to the participants own noral and cofortable paces. For the fast walking speed, the subjects were asked to walk as they were in a hurry for soething in their daily lives. Figure 6.5 and Figure 6.6 show the exaples of acceleration patterns easured fro a PD subject (Figure 6.5) and a healthy subject (Figure 6.6) during walking at their noral self-regulated speed. By direct pattern inspection, the differences of gait paraeters ight not be obvious and interpretable. Figure 6.5 The patterns of accelerations in the vertical and antero-posterior directions easured fro a PD subject 10

11 Figure 6.6 The patterns of accelerations in the vertical and antero-posterior directions easured fro a healthy subject Figure 6.7 and Figure 6.8 show the autocorrelation sequences of the acceleration patterns fro Figure 6.5 and Figure 6.6, respectively. In both the Figure 6.7 and Figure 6.8, the first and second doinant periods on both the autocorrelation sequences of the vertical (VT) and antero-posterior (AP) acceleration patterns alost align with each other at the very exact tie. Coparing the autocorrelation patterns in Figure 6.7 and Figure 6.8, the autocorrelation patterns of the PD subject contain ore signal fluctuations between each doinant period. Moreover, and the doinant peaks on the both VT and AP patterns vary greatly. As Figure 6.8 showing the autocorrelation pattern fro a young healthy subject, the patterns look ore sooth and onotonic. Typically only the autocorrelation sequence of the VT acceleration coponents is used for deriving gait cycle paraeters. The autocorrelation sequence of AP acceleration coponents can be additionally used to better identify the exact peaks of the doinant periods. 11

12 D 2 Blue: VT D 1 Red: AP Figure 6.7 An exaple of the autocorrelation patterns of accelerations along vertical (VT, blue) and anterior- posterior (AP, red) directions of a PD subject D 1 D 2 Blue: VT Red: AP Figure 6.8 An exaple of the autocorrelation patterns of accelerations along vertical (VT, blue) and anterior- posterior (AP, red) directions of a healthy subject Table 6.1 shows the statistical results of the gait-related paraeters fro the 10 test subjects. The average values with its standard deviation of each paraeter are shown here. Note that aong the 5 PD subjects, 2 of the were unable to perfor the 5-eter walks 12

13 above their noral walking speed. Therefore, for the subjects safety only 3 PD subjects participated in the data collection of the 5-eter walks at fast waking speed. The easured data of all the test subjects is listed in Table A6.1 to Table A6.3 in the Appendix of this chapter. Table 6.1 Gait cycle paraeters between PD patients PD Healthy TUG-T tie 23.9±7.9s 10.6±2.2s 5-eter walk Step regularity 0.39± ±0.13 (noral speed) Saples of a step length 30± ±1.85 Stride regularity 0.43± ±0.09 cadence 102.2± ±5.8 syetry 0.97± ± eter walk (fast speed) 6.4 Discussion Step regularity 0.37± ±0.08 Saples of a step length 28.2± ±1.4 Stride regularity 0.47± ±0.08 cadence 108.1± ±6.2 syetry 0.77± ±0.12 In this chapter, the autocorrelation procedure is used to estiate gait cycle paraeters fro trunk acceleration signals easured by the wearable otion detector. Teporal gait paraeters, such as cadence, step regularity, stride regularity and step syetry can be extracted fro the sequence of autocorrelation coefficients of the accelerations. These paraeters can be used to estiate an individual s gait cycle characteristics during walking. In order to investigate the difference of the pattern of autocorrelation coefficients and the gait cycle paraeters between healthy and PD individuals, trunk accelerations during 5-eter walks at noral and fast walking speeds were easured fro 5 healthy subjects and 5 patients with Parkinson s disease. The clinical assessent ethod Tied Up-and Go Test was conducted first to briefly screen the obility of the test subjects. The TUG tie taken by the healthy group is 10.6±2.2s while a longer tie 23.9±7.9s is easured in the PD group. This siple estiate shows a degenerative obility for the PD patients. Coparing the cadences coputed fro the autocorrelation procedure, the PD group has the cadence slightly higher than the healthy group. However, for the PD group the cadence at fast 5-eter walks is 108.1±15.6 step/in., which is approxiately 5.77% increased fro their noral cadences. The healthy group has the cadence of 113.9±6.2 13

14 steps/in. at fast 5-eter walks, which is approxiately 16.94% increased fro their noral cadences. This indicates a liited perforance argin for the PD group due to their degenerative obility. Also note that the larger deviations in the TUG-T and cadences are seen in the PD group due to their varied obility level. For the regularity and syetry of gaits, the PD group has the step regularity of 0.39 ±0.16 and the stride regularity of 0.43±0.2 during walks of noral speed. The healthy group has higher step regularity (0.61±0.14) and stride regularity (0.79±0.09). Siilar perforance can be observed in the walking at fast speed. Therefore, the PD patients have less regular perforance during repeating step and stride perforance copared with the healthy group. Note that the syetry during their noral walking speed in the PD group is higher than that in the healthy group while the syetry during fast walking in the PD group is lower than that in the healthy group. This ixed results regarding gait syetry need further investigations. Several studies have reported the use of the vertical accelerations for autocorrelation procedure [Moe-Nilssen et al. 2004; Keenan et al., 2005; Yang et al., 2010]. In this chapter the accelerations in the vertical (VT), antero-posterior (AP) and edio-lateral (ML) directions were coputed by the autocorrelation procedure to exaine which axis is ost sensitive to otions and produces identifiable pattern related to the gait cycle paraeters. With visual inspection fro the data of the 10 test subjects, the AP and ML coponents are considered to exhibit less sensitive and least descriptive autocorrelation pattern than the VT coponent. Therefore the ML acceleration is not used in the autocorrelation analysis. However, the autocorrelation sequences of AP and ML accelerations were observed to exhibit reduced first doinant period on soe cases though the second doinant period was distinct and deterinable. Fro visual inspection, the autocorrelation sequences coputed fro healthy subjects in this study tend to exhibit ore unifor and onotonic pattern as Figure 6.6 showing the patterns of VT and AP accelerations fro a healthy subject. Referring to Figure 6.5, soe signal fluctuations exist between the doinant periods of the patterns of VT coponents fro a PD patient. Accordingly it iplies less regular oveents between each step, which can be considered the results fro ill-controlled otor behaviors. This requires ore PD data to justify this observation. The purpose of this chapter is to develop a wearable syste for real-tie recognition of Parkinsonian gaits. The proposed syste is able to recognize different teporal gait characteristics fro people with varied obility level. The current focus of interest here is whether those selected gait cycle paraeters can be discriinative between the PD patients and noral healthy people. The PD group and the healthy group show different 14

15 characteristics for the gait cycle paraeters though soe details still needs further investigation. The gait cycle paraeters can be used and a signal processing algorith can be developed for a wearable syste for real-tie recognition of abnoral gaits, like shuffling, or festinating gaits fro PD patents. This developent is expected to benefit and assists abulation rehabilitation of PD patients and personal tele-care applications. Appendix Table A6.1 Tied Up and Go test perforances fro all the subjects Subjects Subject 1 Subject 2 Healthy group Subject 3 Subject 4 Subject 5 Subject 1 Subject 2 PD group Subject 3 Subject 4 Subject 5 Tie (sec.)

16 Table A6.2 Gait cycle paraeters during noral walking speed coputed fro Healthy group PD group the vertical acceleration of all the subjects Subjects D 1 n D 2 Cadence Syetry Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 1 Subject 2 Subject 3 Subject 4 Subject Table A6.3 Gait cycle paraeters during fast walking speed coputed fro the vertical acceleration of all the subjects Subjects D 1 n D 2 Cadence Syetry Subject Healthy group Subject

17 PD group References Subject 3 Subject 4 Subject 5 Subject 1 Subject 2 Subject 3 Subject 4 Subject n/a n/a Berg K.O., Wood-Dauphinee S.L., Willias J.I., Gayton D., Measuring balance in elderly: preliinary developent of an instruent, Physio-therapy Canada, Vol. 41, pp de Melo Roiz R., Azevedo Cacho E.W., Pazinatto M.M., Guiarães Reis J., Cliquet A., Barasnevicius-Quagliato E.M.A., Gait analysis coparing Parkinson s disease with healthy elderly subjects, Arq Neuropsiquiatr, Vol. 68, No. 1, pp Goetz G. C., Poewe, W., Rascol O., Sapaio C., Stebbins G. T, Counsell C., Giladi N., Holloway R. G., Moore C.G., Wenning G.K., Yahr M.D., Seidl L., Moveent Disorder Society Task Force on the Hoehn and Yahr Staging Scale: Status and Recoendations, Moveent Disorders, Vol. 19, No. 9, pp Fahn S., Elton R. L., Mebers of the UPDRS Developent Coittee: Unified Parkinson s Disease Rating Scale. In: Fahn S., Marsden C. D., Calne D. B., Lieberan A, Recent Developent in Parkinson s Disease. Florha Park, NJ: Macillan Healthcare Inforation, pp

18 Feery V., Moretto P., Renaut H., Thévenon A., Lensel G., Measureent of plantar pressure distribution in heiplegic children: changes to adaptive gait patterns in accordance with deficiency, Clinical Bioechanics, Vol. 17, pp Hausdorff J. M., Gait dynaics in Parkinson s disease: Coon and distinct behavior aong stride length, gait variability, and fractal-like scaling, Chaos, Vol. 19, pp Hoehn M. M., Yahr M. D., Parkinsonis: onset, progression and ortality, Neurology, Vol. 17, pp Jovanov E., Wang E., Verhagen L., Fredrickson M., Fratangelo R., defog-a real tie syste for detection and unfreezeing of gait of Parkinson s patients, Proceedings of the 31 st Annual International IEEE EMBS Conference, Minneapolis, MN, USA. Keenan D.B., Wilhel F.H., Classification of locootor activity by acceleration easureent: validation in Parkinson disease, Bioedical Sciences and Instruentation, Vol. 41, pp Lowry K. A., Siley-Oyen A. L., Carrel A. J., Kerr J. P., Walking stability using haronic ration in Parkinson s disease, Moveent Disorder, Vol. 24, No. 2, pp Mathie M., Coster A.C.F., Lovell N.H., Celler B.G., Acceleroetry: providing an integrated, practical ethod for long-ter, abulatory onitoring of huan oveent, Physiological Measureent, Vol. 25, pp. R1-R20. Menz H.B., Latt M.D., Tiedeann A., Kwan M.M.S., Lord S.R., Reliability of the GAITRite walkway syste for the quantification of teporal-spatial paraeters of gait in young and older people, Gait & Posture, Vol. 20, pp Moe-Nilssen R., Helbostad J. L., Estiation of gait cycle characteristics by trunk acceleroetry, Journal of Bioechanics, Vol. 37, pp Moore S.T. MAcDougll H.G., Ondo W.G., Abulatory onitoring of freezing of gait in Parkinson s disease, Journal of Neuroscience Methods, Vol. 167, pp Nelson-Wong E., Howarth S., Winter D.A., Callaghan J.P., Application of autocorrelation and crosscorrelation analyses in huan oveent and rehabilitation research, Journal of Orthopaedic & Sports Physical Therapy, Vol. 30, No. 4, pp

19 Podsiadlo D., Richardson S., The tied Up & Go : a test of basic functional obility for frial elderly persons, J A Geriatr Soc., Vol. 39, pp Yang C.C., Hsu Y.L., A review of acceleroetry-based wearable otion detector for physical activity onitoring, Sensors, Vol. 10, pp Yang M.J., Zheng H., Wang H., McClean S., Hall J., Harris N., Assessing acceleroeter based gait features to support gait analysis for people with coplex regional pain syndroe, Proceedings of the 3 rd International Conference on Pervasive Technologies Related to Assistive Environents, NY, USA. 19