A Design of Self-Assisted Patient Control Technique for Early Rehabilitation Interventions to Prevent the Reduced elbow Joint ROM of COVID-19 Patients

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Abstract

The novel coronavirus, SARS-CoV-2 pandemic has posed new challenges for physiotherapist due to unprecedented acute care patients' surge. It contributed to minimize physical activity, especially reducing the elbow range of motion (ROM). Early rehabilitation and physiotherapy are recommended to combat the adverse effects of extended immobility. However, the increased patient-physiotherapist interaction increased risks of disease transmission. There emerges a need to minimize this interaction and disease transmission probability. This study aims to speed-up the return to regular ROM of COVID-19 patients by developing a self-assisted device for successful elbow therapy. The proposed device referred as 'self-assisted', reflects the idea of active and passive patient intervention and early rehabilitation. The device is designed and programmed to characterize the patients into three levels, depending on their ROM vulnerability: level 1 below 50°, level 2 50°-100° and level 3 above 100°. To examine the efficacy and accuracy of SAPTCOVID-19, eight volunteers with varying ages were selected, who were home-bound due to prolong COVID-19 pandemic and compromised their functional ROM. SAPT-COVID-19 substantially strengthened the elbow ROM for the volunteers and hit the maximum functional ROM over 14-days exercise session, resulting in approximately 10° improvement in elbow ROM. The degree of efficiency of active and passive exercise has also been widely examined. SAPT-COVID-19 is supposed to prevent elbow ROM deterioration and reduce hospitalization, with therapeutic and economic gain and minimized the physiotherapist-patient interactions.

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APA

Ali, S. M. (2021). A Design of Self-Assisted Patient Control Technique for Early Rehabilitation Interventions to Prevent the Reduced elbow Joint ROM of COVID-19 Patients. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CSDE53843.2021.9718473

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