Finger joint continuous interpretation based on sEMG signals and muscular model

Abstract

The human hand is very dexterous and can perform various of gestures in activities of daily living. Only dividing the motions of hand into several types and applying pattern recognition method for implementation of manipulation control may result in low dexterity and delicacy. In this paper, a novel finger joint interpretation method based on sEMG signals and muscular model is presented. The motion of finger is flexion and extension without any external resistant force and at a natural movement velocity. sEMG signals are recorded from flexor digitorum superficialis and extensor digitorum of the forearm. The Hill-based muscular model is used to calculate the force of muscles according to sEMG signals. In this paper, we assume that the changing of force corresponds directly to the motions of fingers given the circumstance that the subjects hold nothing in their hand and keep the movement velocity. The curve fitting method and Kalman filter are implemented to calculate the relation between force and basic movements of digits. Five subjects participated in the experiment to evaluate the efficiency of this method.

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

“…The Kalman filter estimates the current state based on the state of the last moment and estimates the optimal state through the correction of the estimated state and the observed state at the current moment, to obtain the optimal solution and realize the correction of the whole prediction process. Ang et al ( Pang et al, 2013 ) used the Kalman filter to process surface EMG signals and conducted experiments on five subjects. The results showed that the designed Hill muscle model could predict the angle of fingers when they naturally bent.…”

“…Then, a relationship model between sEMG features and the upper limb movement angle was set up. The participants in different trials vary from 5 to 20 according to the studies in ( Pang et al, 2013 ; Geng et al, 2016 ; Wu and Chen 2021 ). The number of subjects in this trial was eight (including seven males, one female), aging between 23 and 32 years old, and six right-hands and two left-hands were included.…”

“…For example, Cavallaro et al (2006) designed an upper limb exoskeleton robot system and used the improved Hill model to conduct continuous movement estimation, and they proposed a 28-channel signal acquisition instrument to obtain sEMG, realizing the continuous angle estimation of the upper limb. Pang et al ( Pang et al, 2013 ) established the Hill model of finger flexion and finger extension through acquired sEMG of the superficial flexor and extensor muscles, and then, they processed sEMG using the Kalman filter. The experiment was conducted on five subjects, suggesting that the Hill model they designed could complete the finger flexion angle estimation.…”

Study on the comparison of three different upper limb motion recognition methods

“…With the development of Electromyography technology, this biological signal detected from contracting muscle has been widely used in biorobots control [1]- [3], body motion recognition [4]- [5], rehabilitation [6] and athlete training. The essential characteristic of EMG signal makes it very convenient and direct to represent status of skeletal muscles.…”

A novel method for elbow joint continuous prediction using EMG and musculoskeletal model

“…INCE the discovery of electromyograms signal in 1666, it has been implemented in many kinds of fields, such as biorobots control [1]- [7], human body motion recognition [8]- [10], rehabilitation [12]- [13] and so on. Comparing with the other signals detected from conventional sensors, such as force sensor and acceleration sensor, EMG signals can reflect the intention of human motion and the electrode which is used to detect EMG signals is relevant small.…”