Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors

Abstract

Background Compensations are commonly observed in patients with stroke when they engage in reaching without supervision; these behaviors may be detrimental to long-term functional improvement. Automatic detection and reduction of compensation cab help patients perform tasks correctly and promote better upper extremity recovery. Objective Our first objective is to verify the feasibility of detecting compensation online using machine learning methods and pressure distribution data. Second objective was to investigate whether compensations of stroke survivors can be reduced by audiovisual or force feedback. The third objective was to compare the effectiveness of audiovisual and force feedback in reducing compensation. Methods Eight patients with stroke performed reaching tasks while pressure distribution data were recorded. Both the offline and online recognition accuracy were investigated to assess the feasibility of applying a support vector machine (SVM) based compensation detection system. During reduction of compensation, audiovisual feedback was delivered using virtual reality technology, and force feedback was delivered through a rehabilitation robot. Results Good classification performance was obtained in online compensation recognition, with an average F1-score of over 0.95. Based on accurate online detection, real-time feedback significantly decreased compensations of patients with stroke in comparison with no-feedback condition (p < 0.001). Meanwhile, the difference between audiovisual and force feedback was also significant (p < 0.001) and force feedback was more effective in reducing compensation in patients with stroke. Conclusions Accurate online recognition validated the feasibility of monitoring compensations using machine learning algorithms and pressure distribution data. Reliable online detection also paved the way for reducing compensations by providing feedback to patients with stroke. Our findings suggested that real-time feedback could be an effective approach to reducing compensatory patterns and force feedback demonstrated a more enviable potential compared with audiovisual feedback.

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

“…Similarly, in the reviewed studies, marker-based motion capture systems were proven to be able to effectively identify compensation. In all, 5 studies have been used as the ground truth for the measurement of the effectiveness of other sensors in compensation detection [ 42 , 43 , 45 , 86 , 87 ]. Several interesting findings were reported using marker-based motion capture systems: (1) pre- and posttests showed that both robotic [ 32 , 38 , 63 ] and MR therapies [ 26 ] elicited benefits on reducing trunk compensatory movements, and stroke survivors showed less trunk compensatory movements during VR reaching [ 24 ].…”

Extended Reality for Enhanced Telehealth During and Beyond COVID-19: Viewpoint

“…Barriers to health care access intensified during the pandemic and XR telehealth emerged as a responsive option. XR telehealth had a particularly significant impact to increase the immediacy of care and access among medically and geographically isolated populations who required continual rehabilitation services [ 62 , 63 ]. In addition to providing immersive and accessible care, XR telehealth connected people in virtual spaces to combat social isolation and maintain health-promoting social relationships over distances [ 64 ].…”

Development of a Compensation-aware Virtual Rehabilitation System for Upper Extremity Rehabilitation in Community-Dwelling Older Adults with Stroke

“…Recent studies start addressing the compensation in the design of VR-based rehabilitation through applying various motion tracking technology [16,17, 18] as well as the pressure sensors [19,20] to detect compensatory movements when stroke survivors perform the UE reaching tasks. For example, Foreman and Engsberg [17] utilized one commercial Kinect device in their VRShape system to measure and shape the trunk compensatory movement during repetitive upper limb practices of a reaching task.…”

“…For example, Cirstea and Levin [10] found a signi cant correlation between the increased trunk involvement with a reduction in active ranges of elbow extension and shoulder exion when stroke individuals were asked to perform the reaching task. The monitoring of stroke survivor's compensation during motor practice is especially important at the context of community where the close supervision of therapist is usually missed.Recent studies start addressing the compensation in the design of VR-based rehabilitation through applying various motion tracking technology [16,17, 18] as well as the pressure sensors [19,20] to detect compensatory movements when stroke survivors perform the UE reaching tasks. For example, Foreman and Engsberg [17] utilized one commercial Kinect device in their VRShape system to measure and shape the trunk compensatory movement during repetitive upper limb practices of a reaching task.…”

“…Besides the trunk compensation, the affected limb could play a role in compensation, for example, the affected upper arm would compensate the lower arm motion in elevating the upper limb [21]. Cai's studies [19,20] could further detect the shoulder elevation besides trunk compensations using the pressure mattress sit under the stroke survivors. Thus, to reduce the compensation in the UE motor practice, it is necessary to measure both the trunk and upper limb compensations.…”