Assistive Robotic Hand Orthosis (ARHO) controlled with EMG: evaluation of a preliminary prototype

Farinha, Diogo; Dias, João Marcos Domingues; Neves, Pedro; Pereira, Kátia; Ferreira, Carlos; Pires, Gabriel

doi:10.1109/enbeng.2019.8692562

Cited by 5 publications

(1 citation statement)

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“…Scientists also identified the following advantages of surface EMG during rehabilitation: that the electrodes are easier to apply, the patient feels minimal discomfort or pain, skin stretching or allergy when using them, the signals are better reproduced and they are very suitable for various movement recognition and analysis during exercising or training. However, the surface electrodes, which, in most cases, cover a larger area, take more information from the adjacent muscles, which can affect the accurate measurement results, which is very important in physical training assessment, recovery effectiveness or movement recognition and analysis in pathological conditions, especially after stroke, brain trauma, or spinal cord injuries [60].…”

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%