Performance monitoring and evaluation of patients after stroke in free-living using wearable motion sensors and digital biomarkers
|Title||Performance monitoring and evaluation of patients after stroke in free-living using wearable motion sensors and digital biomarkers|
|Abstract||Patients after stroke often face long-term disability due to hemiparesis and thus require rehabilitation. With ageing societies, the stroke incidence is expected to increase, even among people who are in the workforce. Hence, costs for healthcare systems will rise. The current situation in stroke rehabilitation could intensify, more patients require treatment while at the same time a shortage of clinical personnel1 becomes apparent.
Wearable motion sensors, including inertial measurement units (IMUs), have the potential to mitigate challenges in stroke rehabilitation and offer great potential for reshaping healthcare. With digital biomarkers derived from wearable sensors, e.g. to describe gait parameters or motion intensity, clinicians and patients could be supported during the rehabilitation. For example, objective movement quantification might help clinicians adapting therapies to the individual needs of a patient after stroke.
State-of-the-art performance monitoring and evaluation is restricted to guided short-term measurements that follow defined assessment tasks in clinical environments, which are subjectively assessed by clinicians. Remote monitoring and evaluation of patients after stroke in free-living and the potential of wearable sensors is insufficiently addressed in research. Therefore, solutions for continuous and objective performance monitoring and evaluation using wearable motion sensors and algorithms are sought.
The aim of this thesis is to devise and evaluate new solutions for longitudinal performance monitoring and evaluation in patients after stroke using algorithms and digital biomarkers, which could be used in freeliving.
We test the following hypotheses: 1. IMUs, (machine learning) algorithms, and digital biomarkers
are viable for longitudinal performance monitoring in patients after stroke. 2. Motion performance differences in the affected and less-affected upper and lower body-sides can be evaluated during therapies and free-living using IMU data and digital biomarkers.
To test the hypotheses, a six month, longitudinal clinical observation study with eleven hemiparetic patients after stroke was implemented. In a novel study design, outpatients were followed by the examiner and more than 620 hours of motion data were recorded and annotated using a smartphone application. In full-day recordings, patients followed their therapy and performed various activities of daily living while wearing six body-worn IMUs. In addition, we used digital twins for personalised movement analyses in two case studies, including athletes and patients after stroke.
This thesis includes eight peer-reviewed scientific publications, addressing four specific goals: (1) to review wearable motion sensors and machine learning algorithms for clinical assessment score estimation, (2) to implement activity primitive extraction algorithms for clinical score estimation and trend analysis, (3) to develop and evaluate digital biomarkers for performance analysis, and (4) to investigate digital twins for movement analysis and the evaluation of wearable sensor systems and algorithms.
Wearable motion sensors and machine learning algorithms for clinical score estimation were reviewed. The review showed that mainly accelerometers for measurements were deployed and that score estimation algorithms included classification or regression-based machine learning techniques.
Rule-based algorithms for activity primitive extraction from continuous sensor data were implemented. We showed that the Extended Barthel Index (EBI) can be estimated with approx. 12% relative error on average using support vector regression and leave-one-participant-out cross-validation. Further, the analysis of activity primitives revealed patient-specific recovery trends.
The convergence point (CP), a newly developed digital biomarker for longitudinal, bilateral trend analysis revealed patient-specific recovery trends. In addition, the physical activity (PA) and functional range of motion (fROM) was analysed. The CP, PA, and fROM confirmed that differences in affected and lessaffected upper and lower body can be quantified during rehabilitation, including therapies and free-living.
Finally, we present a novel methodology based on biomechanical simulations and motion data synthesis for the systematic evaluation of wearable sensor systems, algorithms, and digital biomarkers using personalised digital twins.
|# of Pages||159|