sEMG signal based hand and finger movement clasification using different classifiers and techniques : A Review

Varshney, Shivi; Thakur, Ritula; Jigyasu, Rajvardhan; Narayan, Yogendra

doi:10.1109/iccs45141.2019.9065848

Cited by 1 publication

(1 citation statement)

References 52 publications

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“…Several papers in the literature have focused on the use of EMG signals in the medical context [ 15 , 16 , 17 , 18 , 19 ]. In [ 17 ], for example, the potential clinical value of sEMG-based techniques in rehabilitation medicine with a focus on neurorehabilitation has been addressed.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform

Leone

Rescio

Manni

et al. 2022

Sensors

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Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different “confidence” levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an “augmented” dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 “confidence” levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia “confidence” levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.

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