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In this study, the detection of abnormal knee movements during walking is addressed by analyzing electromyographic (EMG) signals. To this end, data were collected from individuals with and without knee anomalies, with the purpose of identifying distinctive patterns that allow us to distinguish between normal and abnormal movements. Artificial intelligence models were used to perform anomaly detection. Time domain feature extraction techniques were used in the process. The processed data is directly input to the Support Vector Machine, K-mean and Random Forest models for learning and classification. The results show that the best results are obtained by the random forest which shows an accuracy of up to 98 \(\%\) to classify abnormal movements in the rectus femoris. With the aim of seeking an effective and quick recovery to return to their daily activities safely and successfully, for people facing meniscus wear and the like.

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Obtaining and Analyzing EMG Signals for the Detection of Anomalies and Rehabilitation of Knee Movement Problems

  • 会议论文

  • 发表日期：

    2026年

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  • Alma Y. Alanis,
  • Hannia M. Hernandez,
  • Arturo Valdivia-G,
  • Oscar D. Sanchez

摘要

In this study, the detection of abnormal knee movements during walking is addressed by analyzing electromyographic (EMG) signals. To this end, data were collected from individuals with and without knee anomalies, with the purpose of identifying distinctive patterns that allow us to distinguish between normal and abnormal movements. Artificial intelligence models were used to perform anomaly detection. Time domain feature extraction techniques were used in the process. The processed data is directly input to the Support Vector Machine, K-mean and Random Forest models for learning and classification. The results show that the best results are obtained by the random forest which shows an accuracy of up to 98 \(\%\) to classify abnormal movements in the rectus femoris. With the aim of seeking an effective and quick recovery to return to their daily activities safely and successfully, for people facing meniscus wear and the like.