Factors affecting real‐time evaluation of muscle function in smart rehab systems

Abstract

Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross‐reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real‐time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.

AI-Powered Camera and Sensors for the Rehabilitation Hand Exoskeleton

“…Given the widespread use of camera-based solutions across different systems [12,13], we have adopted this approach in our design. For projects focused on image analysis, single-board computers like the Jetson Nano [14][15][16][17][18], Google Coral [19,20], and Raspberry Pi [21][22][23] are favored for their compact size and low power consumption. To optimize our system, we have used the Google Coral Dev Board Mini to run image processing and control peripherals because of its smaller size and inclusion of a Tensor Processing Unit (TPU) for faster processing of deep learning models.…”