Pressure Insoles for Gait and Balance Estimation

Stroke leads to impairment in motor ability, gait, and balance, due to brain tissue damage [1]. Clinical therapy following stroke aims at improving mobility and functional capacity. However, there is lack of objective information about subject’s performance once they are transferred home [2]. A wearable, unobtrusive system is needed to describe and compare clinical capacity and performance in a home setting. ForceShoes™ (Xsens Technologies B.V., The Netherlands) had been developed to provide holistic information about subject’s gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and Dynamic Stability Margin (DSM) [3], [4]. Using these measures, a clear distinction between the capacity and performance of the subject is seen. However, this system is obtrusive and requires a long time to set up. This project addresses the need for a wearable and minimal sensing system with an unobtrusive set up. Pressure insoles are lightweight and inconspicuous, and when coupled with an Inertial Measurement Unit (IMU), several gait and balance measures can be estimated. In this study, a 1-D pressure insole system (medilogic ® insoles, T&T medilogic Medizintechnik GmbH, Germany), coupled with IMUs, is investigated for objective quantification of gait and dynamic balance measures. Although, to obtain such measures, 3D forces and moments are required. Linear regression models were used to model 3D forces/moments from the 1D plantar pressures measured from pressure insoles. The predicted forces and moments were used for estimation of XCoM and DSM. These parameters were compared with the estimations done by the forces and moments from the Force Shoes™. The regression model is tested for different walking speeds. High correlation and low differences between the estimations from predicted and measured values show that pressure insoles can indeed be used as an wearable alternative. The results will also be used in designing a wearable in-shoe system that can be used in daily life monitoring for stroke subjects. The study is a part of project 7 of NeuroCIMT, funded by the Dutch National foundation STW. REFERENCES [1] S. F. Tyson, M. Hanley, J. Chillala, A. Selley, and R. C. Tallis, “Balance disability after stroke.,” Phys. Ther., vol. 86, no. 1, pp. 30–38, 2006. [2] B. Klaassen, B.-J. F. van Beijnum, M. Weusthof, D. Hof, F. B. van Meulen, Ed Droog, H. Luinge, L. Slot, A. Tognetti, F. Lorussi, R. Paradiso, J. Held, A. Luft, J. Reenalda, C. Nikamp, J. H. Buurke, H. J. Hermens, and P. H. Veltink, “A Full Body Sensing System for Monitoring Stroke Patients in a Home Environment,” Commun. Comput. Inf. Sci., vol. 511, pp. 378–393, 2016. [3] F. B. van Meulen, D. Weenk, E. H. F. van Asseldonk, H. M. Schepers, P. H. Veltink, and J. H. Buurke, “Analysis of Balance during Functional Walking in Stroke Survivors,” PLoS One, vol. 11, no. 11, p. e0166789, Nov. 2016. [4] F. B. van Meulen, D. Weenk, J. H. Buurke, B.-J. F. van Beijnum, and P. H. Veltink, “Ambulatory assessment of walking balance after stroke using instrumented shoes,” J. Neuroeng. Rehabil., vol. 13, no. 1, p. 48, 2016. [5] A. L. Hof, M. G. J. Gazendam, and W. E. Sinke, “The condition for dynamic stability,” J. Biomech., vol. 38, no. 1, pp. 1–8, 2005.