Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements

Abstract

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury

“…Grand averages of grasp predictors were at 75.39 ± STD 13.77% (participants with SCI) and 97.66 ± STD 5.48% (control group), respectively, and simulation runs showed that our multimodal approach performed significantly better (p < 0.05 for both comparisons) than isolated EMG or IMU information for grasp type predictions in online settings. We could exceed the grasp decoding performance of similar, EEG-based work with persons with SCI [46] and, although not directly comparable due to different experimental paradigms, the results of real-time EMG-based decoding of 10 individual hand and wrist movements in non-disabled people [29] and of EEG-based grasp decoders for non-disabled persons with SCI [10,11]. Our control group models further performed similar to or better than the hand gesture classification models of Vásconez et al [25].…”

“…Generally, the performance of an HMI does not solely rely on appropriate signal capture and feature engineering but also on the selection of suitable processing methods such as machine learning [25,26,29]. Previous experiments have shown that tree-based models such as Random Forests seem to perform well in a supervised multimodal HMI for activity recognition [24].…”

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

“…Using an MLP model, accuracies of 0.912 and 0.609 were achieved for offline and real-time tests, respectively. Similarly, Abbaspour et al [ 58 ] classified ten hand gestures using four EMG channels and demonstrated significant difference in accuracy between the offline and real-time decoding. They tested nine different machine learning algorithms, including MLP, and all of them resulted in a substantial decrease in accuracy.…”

“…These results suggest that offline performance does not necessarily translate to real-time systems. Abbaspour et al [ 58 ] suggested that the difference in accuracy could be decreased by having subjects practice the gestures. More studies on various aspects, including consistency in muscle contraction, algorithm optimization, and evaluation, will be conducted in the future to improve real-time performance.…”

A Motor Unit Action Potential Based Signal Decomposition Scheme for Myoelectric Pattern Recognition System

“…Discrete wavelet transform [26] and onset based algorithms [27][28][29][30] are implemented to reduce the effect of noise and detecting the correct movement. The next step is calculating the segments from the pre-processed signals to extract features [31,32]. These features indicate EMG signal characteristics and are used for post-processing and real-time classification.…”