IEEE J Biomed Health Inform - Dependence Independence Measure for Posterior and Anterior EMG Sensors Used in Simple and Complex Finger Flexion Movements: Evaluation Using SDICA.


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Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.

Resumo Limpo

identif simpl complex finger flexion movement use surfac electromyographi semg muscl activ strategi necessari control humancomput interfac prosthesi orthos order identifi movement semg sensor place anterior posterior muscl compart forearm general accuraci myoelectr classif depend sever factor includ number sensor featur extract method classif algorithm myoelectr classif use minimum number sensor optim electrod configur alway challeng task sometim use sever sensor includ high densiti electrod will guarante high classif accuraci research investig depend independ natur anterior posterior muscl simpl complex finger flexion movement outcom research show posterior part hand muscl depend henc respons simpl finger flexion hand studi show anterior muscl respons complex finger flexion also indic simpl finger flexion can identifi use semg sensor connect anterior muscl make posterior placement either independ redund vice versa true complex action can easili identifi use semg sensor posterior muscl result studi benefici optim electrod configur design prosthet relat devic use minimum number sensor

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