Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

Abstract

Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.

Simple recognition of hand gestures using single-channel EMG signals

“…2 Electromyography (EMG) is a method for recording muscle electrical activity, 12 the acquisition of EMG signals from a person's skin surface is known as surface electromyography (SEMG), whereas the acquisition of EMG signals from a person's muscles is known as intramuscular electromyography. 13 EMG signals have shown positive potential for prosthesis control in a variety of sectors, including assistive technology and rehabilitation sciences, 14 actually, they are becoming vital biological characteristics, with applications in human-machine interface, prosthetic device development, and rehabilitation equipment. 12 Moreover, EMG provides valuable insights into neuromuscular function and provides detailed motor control data that can be beneficial for both diagnostic purposes and the development of prosthetic appliances.…”

“…Machine learning technology is used to solve these problems in the control of the myoelectric system, in fact, to evaluate features and categorize various limb motions for improved prosthesis executions, pattern recognition, and machine learning techniques have been found to be very important. 14 Data pre-processing, feature extraction, and classification are three levels for pattern recognition, which is generally used in machine learning. 20,21 Applying various pre-processing methods, such as amplification and filtering, and various post-processing techniques like smoothing, can significantly improve the overall outcome.…”

“…31 In a study, 31 seven individual hand gestures and three whole-hand gestures were performed by 10 healthy subjects, to make the recognition system uncomplicated and easier to use, they tried to restrict the number of channels used to record EMG signals. Three channels, which are few in comparison to those utilized in the other research, 14,[34][35][36] were used on three distinct muscles of the forearm to record EMG signals. Each channel yielded six time-domain (TD) features.…”

“…EMG signals have shown positive potential for prosthesis control in a variety of sectors, including assistive technology and rehabilitation sciences, 14 actually, they are becoming vital biological characteristics, with applications in human-machine interface, prosthetic device development, and rehabilitation equipment. 12 Moreover, EMG provides valuable insights into neuromuscular function and provides detailed motor control data that can be beneficial for both diagnostic purposes and the development of prosthetic appliances.…”