Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees

Author:

Al-Timemy Ali,Bugmann Guido,Escudero JavierORCID

Abstract

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signal increases. We demonstrate our framework utilizing EMG datasets collected from nine transradial amputees who performed nine movement classes with Time Domain Power Spectral Descriptors (TD-PSD), Wavelet and Time Domain (TD) feature extraction (FE) methods and a Linear Discriminant Analysis (LDA) classifier. Nonetheless, the concept can be applied to other types of features and classifiers. In addition, the proposed framework is validated with different movement and EMG channel combinations. The results indicate that the proposed framework works well with different FE methods and movement/channel combinations with classification error rates of approximately 13% with TD-PSD FE. Thus, we expect our proposed framework to be a straightforward, yet important, step towards the improvement of the control methods for upper-limb prostheses.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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1. Decision-Change Informed Rejection Improves Robustness in Pattern Recognition-Based Myoelectric Control;IEEE Journal of Biomedical and Health Informatics;2023-12

2. Decoding transient sEMG data for intent motion recognition in transhumeral amputees;Biomedical Signal Processing and Control;2023-08

3. Improving the Hand Movement Classification Accuracy through EMG based on Histogram Feature Enhancement from Time Domain Representation;2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT);2023-04-05

4. Recalibration of myoelectric control with active learning;Frontiers in Neurorobotics;2022-12-15

5. Hand movements classification based on Myo armband signals;2022 4th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE);2022-11-26

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