Wearable motion sensors and digital biomarkers in stroke rehabilitation
|Wearable motion sensors and digital biomarkers in stroke rehabilitation
|Introduction: Wearable motion sensors and personalised digital biomarkers could revolutionise stroke rehabilitation. In this work, we propose three novel digital biomarkers for the longitudinal performance monitoring and movement evaluation of
hemiparetic patients after stroke that could be used in freeliving.
Methods: We introduce convergence points (CP) as a marker family that describe the relation of motion between body sides across time to predict a virtual recovery point using regression techniques. The regressionbased CP estimation interprets continuously recorded IMU data, i.e. gait parameters, including stride count and stride duration, which are
Results: In an observational clinical study, including 11 outpatients after stroke, we derived more than 620 hours of annotated movement data to investigate activities in therapy and freeliving. CP estimates revealed inter-patient variability within and across gait parameters and that the patient behaviour was influenced by individual therapy schedules. The PA
Conclusion: Our digital biomarkers, which are specifically designed for longitudinal stroke rehabilitation, hold promise for applications in freeliving.
|Current Directions in Biomedical Engineering