sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Abstract

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System

“…Currently, the mainstream application of myoelectric signal feature recognition algorithms can be broadly categorized into three groups: time-domain methods, frequency-domain methods, and time-frequency domain methods [18]. Time-domain features have the advantage of direct and reliable extraction from the original sEMG signal data, enabling straightforward extraction from time series data sets without necessitating additional data conversion or processing by the system [19]. Hence, this method offers simplicity, ease of system design and implementation, as well as efficiency in system calculation, resulting in a relatively light system workload.…”

“…The remaining hand gestures also demonstrated satisfactory recognition rates, averaging 90%. The higher recognition rates for fist shake and fist spread gestures can be attributed to the enhanced responsiveness of the selected test muscle regions to these specific gestures compared to the other movements [19]. Comparing the results of other studies, Ding et al [28] used a CNN method for gesture recognition with an average recognition rate of 78.86%, another scholar used a method based on CNN-LSTM for gesture recognition with an average recognition rate of 87%, and Huang et al [29] used an improved deep forest method for their gesture recognition test with an average recognition accuracy of 94.14%.…”

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

“…For example, robust hand gesture recognition based on two-electrode sEMG systems and SVM algorithms has been reported, where overall classification accuracies of over 90% [ 22 ] and 97% [ 23 ] were achieved. However, a recent review [ 24 ] reporting the use of sEMG signals to classify hand gestures highlighted the lack of focus on two-sEMG-electrode systems in the field. Additionally, classification accuracy based on two-sEMG-electrode systems was reported [ 25 ] to vary by up to 20%, depending on machine learning algorithms utilized.…”

Simultaneous Estimation of Hand Configurations and Finger Joint Angles Using Forearm Ultrasound

“…Deep Learning has also been used for facilitating sEMGbased human computer interaction [28]. Tosin et al provide a current review of sEMG-based upper limb movement classifiers [29] and Palumbo et al provide a review of biopotential signal monitoring in rehabilitation [30]. Several commercial solutions have been created based on sEMG data processing for hand motion prediction in the context of prosthetics including Coapt Gen2 and Myo Plus Ottobock.…”

“…Muscle fatigue produces a decrease in the mean frequency and has a variable effect on the sEMG signal amplitude [32]. These biological signal limitations in addition to signal contamination due to motion artifact, and electromagnetic, and environmental interference might introduce variation in time, frequency, and statistical properties of the signal and are an impediment to easy adoption [29].…”