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<p>An algorithm for classifying human lower limb movement patterns to control bioelectric prostheses for above-hip amputations is implemented. This includes solution of problems associated with pre-processing the raw data, such as signal filtration, EMG signal segmentation, and feature extraction. This is followed by a&#xa0;normalization and classification module. The study addresses both classical machine learning methods (SVM, knn, DT) and neural networks, namely the standard feedforward network.</p>

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Recognition of human lower limb movement patterns

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • K. V. Eidelman

摘要

An algorithm for classifying human lower limb movement patterns to control bioelectric prostheses for above-hip amputations is implemented. This includes solution of problems associated with pre-processing the raw data, such as signal filtration, EMG signal segmentation, and feature extraction. This is followed by a normalization and classification module. The study addresses both classical machine learning methods (SVM, knn, DT) and neural networks, namely the standard feedforward network.