Assessing quality of upper limb movements after stroke with prospects of wearable technologies

Assessing upper limb movement quality that can be affected in subjects after a cerebrovascular stroke event, is important to improve the understanding in the underlying pathomechanisms due to the stroke and identifying possible treatment approaches. Stroke-related altered movement performance includes weakness and a loss of interjoint coordination that are usually assessed by clinical scales with limited information about movement quality. This thesis aimed to investigate the applicability of wearable technologies to assess upper limb movement quality after stroke with an emphasis on interjoint coordination and different movement contexts.
Chapter 2 presents a summary of the state of the art in upper limb kinematic assessments after stroke, by considering the movement task, metric, and the available psychometric properties.
Chapter 3 reports on kinematic metrics of interjoint coordination with respect to the task condition and impairment severity.
Chapter 4 further provides new estimates of reduced upper limb movement variability by use of dimension-reduction approaches on a 7 DOF upper limb model.
Chapter 5 addresses the differences in upper limb kinematic expression due to the movement task type. Furthermore, the upper limb kinematics were compared on the level of common movement sub-phases across different movement tasks.
Chapter 6 concerns the effects of arm load and target height on upper limb kinematics from the trunk to the finger digits during functional reach-to-grasp movements.
In this thesis, the detection of different kinematic parameters to quantify aspects of movement quality, such as interjoint coordination in subjects with stroke-related upper limb impairments was shown to be possible by use of a full-body wearable sensing suit providing new insights into the impairments. Spatiotemporal measures, such as joint range of motions, peak velocity and smoothness measures provided added information of movement quality to standard clinical scales. The analysis of different upper limb movement tasks, such as isolated joint motions, gestures, and reach-to-grasp movements, as well as the effects of task conditions, such as the arm load and target position resulted in different kinematic expressions. The findings are useful with respect to establishing upper limb kinematic assessment standards and discovering new and relevant treatment contents by use of wearable sensing.