Moving On:Measuring Movement Remotely after Stroke

Most persons with stroke suffer from motor impairment, which restricts mobility on one side, and affects their independence in daily life activities. Measuring recovery is needed to develop individualized therapies. However, commonly used clinical outcomes suffer from low resolution and subjectivity. Therefore, objective biomechanical metrics should be identified to measure movement quality. However, non-portable laboratory setups are required in order to measure these metrics accurately. Alternatively, minimal wearable systems can be developed to simplify measurements performed at clinic or home to monitor recovery. Thus, the goal of the thesis was ‘To identify metrics that reflect movement quality of upper and lower extremities after stroke and develop wearable minimal systems for tracking the proposed metrics’. Section Upper Extremity First, we systematically reviewed literature ( Chapter II ) to identify metrics used to measure reaching recovery longitudinally post-stroke. Although several metrics were found, it was not clear how they differentiated recovery from compensation strategies. Future studies must address this gap in order to optimize stroke therapy. Next, we assessed a ‘valid’ measure for smoothness of upper paretic limb reaching ( Chapter III ), as this was commonly used to measure movement quality. After a systematic review and simulation analyses, we found that reaching smoothness is best measured using spectral arc length. The studies in this section offer us a better understanding of movement recovery in the upper extremity post-stroke. Section Lower Extremity Although metrics that reflect gait recovery are yet to be identified, in this section we focused on developing minimal solutions to measure gait quality. First, we showed the feasibility of 1D pressure insoles as a lightweight alternative for measuring 3D Ground Reaction Forces (GRF) ( Chapter IV ). In the following chapters, we developed a minimal system; the Portable Gait Lab (PGL) using only three Inertial Measurement Units (IMUs) (one per foot and one on the pelvis). We explored the Centroidal Moment Pivot (CMP) point ( Chapter V ) as a biomechanical constraint that can help with the reduction in sensors. Then, we showed the feasibility of the PGL to track 3D GRF ( Chapters VI-VII ) and relative foot and CoM kinematics ( Chapter VIII-IX ) during variable overground walking by healthy participants. Finally, we performed a limited validation study in persons with chronic stroke ( Chapter X ). This thesis offers knowledge and tools which can help clinicians and researchers understand movement quality and thereby develop individualized therapies post-stroke

Ambulatory Estimation of XCoM using Pressure Insoles and IMUs

Ambulatory gait assessment using minimal sensors has quite an impact for different applications requiring localised sensing. ForceShoes™ was developed as one such solution. It consists of two IMUs, and two 6DoF force and moment (F&M) sensors on each foot1. Additionally, an ultrasound system was added 2. The complete system, also referred to as Ambulatory Gait and Balance System (AGBS), is used to measure ambulatory kinematics and kinetics of the feet while walking. The AGBS has been validated against standard systems2,3. Using the measured F&M, and position estimations from IMUs, the low and high-frequency information of Center of Mass (CoM) is estimated. This was used to estimate the Extrapolated Center of Mass (XCoM)4. XCoM along with base of support provides information about stability during walking4. The unique advantage of the AGBS is its portability and ambulatory measurement when compared to standard systems. The F&M sensors in the AGBS however, are quite bulky, making it heavier and taller than normal shoes. As an alternative, using 1D pressure sensors was studied. Pressure sensors are thin and easy to slip as insoles in shoes. Therefore, they show potential in making the ambulatory system less bulky

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