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Session: Movement Sensing - Balance - Fall Detection
Session starts: Friday 25 January, 10:30
Presentation starts: 11:45
Room: Lamoraalzaal

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Sietse Rispens (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)
Kimberley van Schooten (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)
Mirjam Pijnappels (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)
Andreas Daffertshofer (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)
Peter Beek (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)
Jaap van Dieën (MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, the Netherlands.)

Abstract:
Algorithms, based on movement patterns obtained from inertial sensors, can be used to quantify dynamic instability [1] and increased fall risk [2]. As it stands, fall risk can only be estimated under controlled conditions and on a substantial set of steady-state data. However, features extracted from trunk accelerations during daily life might improve risk estimations, since these features are based on situations in subjects’ real life and they can be complementary to questionnaire or laboratory based fall risk predictions. This study therefore aims to discover what measures, based on daily life trunk accelerations, are reliable and associated to self-reported fall history. One week of trunk acceleration data was obtained with the MoveMonitor (McRoberts) in 29 older adults. From these data, we extracted the locomotion episodes, from which a number of measures, including maximum Lyapunov exponents, inter-stride variabilities and spectral features were calculated. Associations between these measures and self-reported number of falls in the preceding year were calculated by Pearson’s correlation. Reliability of the measures obtained from walking episodes during weekly activities was assessed by intra class correlations (ICC), comparing first and second measurement weeks of 56 subjects. The best association with fall risk was shown by the relative amount of spectral power below 0.5 Hz in the medio-lateral accelerations, having a Pearson’s correlation of 0.62 (p=0.0004) with the self-reported number of falls in the preceding year and an ICC of 0.80. Other features showing a substantial correlation with fall history were the median stride time (correlation 0.38 (p=0.047), ICC 0.92) and the median duration of the locomotion episodes (correlation 0.41 (p=0.030), ICC 0.52). From the results of our study we conclude that daily life trunk accelerations contain reliable information, which, if validated by future research, may contribute to fall risk assessment. [1] J. B. Dingwell, et al., "Local dynamic stability versus kinematic variability of continuous overground and treadmill walking," Journal of Biomechanical Engineering-Transactions of the Asme, vol. 123, pp. 27-32, Feb 2001. [2] M. J. P. Toebes, et al., "Local dynamic stability and variability of gait are associated with fall history in elderly subjects," Gait & Posture, vol. 36, pp. 527-531, 2012.