Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges

Abstract

Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.

Personalized robots for long-term telerehabilitation after stroke: a perspective on technological readiness and clinical translation

“…Beyond leveraging state-of-the-art technology to lessen the need for professional intervention via remote monitoring, the technological preparedness of post-stroke personalized rehabilitation robots should also focus on the provision of tailored adaption and autonomous theranostics, congruent with the patient's unique requirements. Technological readiness should prioritize enhancing robot autonomy through cutting-edge artificial intelligence (AI) technology, which can learn from individual patient data, e.g., EMG ( 41 ), EEG ( 42 ), electrical impedance myography ( 43 ), etc., enabling the adaptation of more personalized telerehabilitation and offering automatic theranostics over long-term service. It presents an economical and timely method for bridging the gap between professionals and patients for diagnosis, treatment, and follow-up, which is unconstrained by geographical limitations.…”