Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

Abstract

The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

“…For the selection of classification models, LDA and SVM models were widely applied in HMI based on gesture recognition of their simple structure, high computational efficiency, and robustness [36]- [38]. This paper chose LDA and SVM (polynomial kernel) to classify different gestures and force levels.…”

Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

“…Typically, there is some period of time given to transition between gestures, which is flagged with a ‘Null’ label. This allows for three primary sources of error in labelling (i) users do not instantaneously transition between gestures and as a result many of the ‘Null’ samples will be members of actual gesture classes, (ii) user fatigue can lead to variation in production of gestures confounding a trained model [ 7 , 8 , 14 , 15 ], (iii) users may inadvertently make incorrect gestures [ 8 , 16 ]. The solutions presented in this paper focus on the first two issues.…”

“…Up to 75% of the samples in a given data set can be ‘Null’ gestures which do not a gesture category included in the model [ 17 ]. The majority of contemporary studies reviewed for this paper indicated they discarded transient data in beginning/in between/at end of gestures, or indicated that transitions between gestures received some manual processing [ 8 , 9 , 10 , 11 , 12 , 15 ]. This can lead to unrealistically optimistic performance estimates.…”

“…It is commonly observed that gesture production is subject to variability over time [ 8 , 14 , 15 ]. If a user finds a gesture difficult or uncomfortable to make, their production of the gesture may degrade over time or they may assume different wrist positions to ease gesture production [ 14 , 15 ].…”

“…It is commonly observed that gesture production is subject to variability over time [ 8 , 14 , 15 ]. If a user finds a gesture difficult or uncomfortable to make, their production of the gesture may degrade over time or they may assume different wrist positions to ease gesture production [ 14 , 15 ]. These physical adaptions can lead to some gesture classes strongly resembling each other over multiple repetitions as the user behavior does not match the assigned label.…”