An EMG-based Gesture Recognition for Active-assistive Rehabilitation

Cases, Carlos Matthew P.; Baldovino, Renann G.; Manguerra, Michael V.; Dupo, Voltaire B.; Dajay, Ryan Christoper R.; Bugtai, Nilo T.

doi:10.1109/hnicem51456.2020.9400132

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Gesture recognition based on EMG is a method that allows dynamic evaluation of muscle work in real time. EMG is focused on electrical signals from muscles that are controlled by the cerebral system and are generated during muscle contraction, the signal reflects physiological properties of the muscles; the EMG signal is the motor electrical activity of the muscles [58]. Nowadays, in rehabilitation, it is preferable to use surface EMG signals are recorded with non-invasive electrodes detected at the surface and to obtain information about the activation time or intensity of surface muscles.…”

Section: Discussionmentioning

confidence: 99%

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

et al. 2023

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One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

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Section: Discussionmentioning

confidence: 99%