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<p>In this investigation, a research of the surface electromyography (sEMG) based feature evaluation method is presented for children with cerebral palsy (CP). The muscle activity sensitive features of children with CP during treadmill training were investigated. Time domain, frequency domain, time-frequency domain and entropy feature extraction methods were used to extract the features of nine children with CP participating in this investigation. Our results indicate that there exist thirty-one two-feature sets which are sensitive to the muscle activity of children with CP and have good generalization through nine subjects. Therefore, the feature sets obtained in this research can be used to pattern recognition (such as gait phase recognition, training process recognition) of children with CP. The concluded thirty-one two-feature sets in this study are sensitive to the way how the perturbation has directly affected muscle activity. Therefore, these feature sets can be used to examine the outcome of training on the treadmill and may have potential applications in clinical decision making.</p>

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Extraction of lower limb muscle activity sensitive EMG features: a novel application in children with cerebral palsy rehabilitation device

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Pengna Wei,
  • Yanzheng Lu,
  • Jiandong Li,
  • Jue Qu

摘要

In this investigation, a research of the surface electromyography (sEMG) based feature evaluation method is presented for children with cerebral palsy (CP). The muscle activity sensitive features of children with CP during treadmill training were investigated. Time domain, frequency domain, time-frequency domain and entropy feature extraction methods were used to extract the features of nine children with CP participating in this investigation. Our results indicate that there exist thirty-one two-feature sets which are sensitive to the muscle activity of children with CP and have good generalization through nine subjects. Therefore, the feature sets obtained in this research can be used to pattern recognition (such as gait phase recognition, training process recognition) of children with CP. The concluded thirty-one two-feature sets in this study are sensitive to the way how the perturbation has directly affected muscle activity. Therefore, these feature sets can be used to examine the outcome of training on the treadmill and may have potential applications in clinical decision making.