Scholarlink

Purpose <p>To address sensor redundancy and single-dimension (either phase or pattern) in gait recognition, this study proposes a few-channel multi-information fusion framework to achieve concurrent gait pattern-phase recognition, balancing recognition accuracy, system simplicity, and user comfort.</p> Methods <p>Ten healthy males performed five gait patterns (level walking, stair ascent/descent, ramp ascent/descent). Lower limb surface electromyogram (sEMG), acceleration, and plantar pressure signals were collected. Gait cycles were split into five phases using plantar pressure signals, features were extracted from intra-phase sEMG and acceleration signals, selected via Maximum Relevance Minimum Redundancy (mRMR), and classified by Extreme Gradient Boosting (XGBoost) for gait recognition.</p> Results <p>Firstly, a study of five-class classification of gait phases for one single terrain using sensor data from one single muscle location showed that signals at the medial calf (gastrocnemius) or thigh (rectus femoris) had better classification performance with an average accuracy of 91.58%. Secondly, when the five gait phases from five different terrains were combined into a 25-class classification task, the results of fusing the two-channel signals from the calf and the two-channel signals at the thigh were analyzed separately. With fewer channels and features, the classification accuracy reached 88.5%, covering 25 classes.</p> Conclusion <p>Considering that lower leg amputations constitute a significant proportion of all major lower limb amputations, we suggest that the fusion of signals from the rectus femoris and semitendinosus positions as the signal sources should be selected for intelligent prosthesis control.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-phase and Multi-mode Gait Recognition with Few Channels and Few Features Based on the Fusion of sEMG and Acceleration

  • 期刊论文

  • 发表日期：

    2026年4月19日

查看全文
  • Songbo Zhou,
  • Chaoming Luo,
  • Xinyi Ma,
  • Yongxin Lv,
  • Na Ta,
  • Junfeng Gao,
  • Zhengyi Li,
  • Dandan Yang,
  • Hui Zhou

摘要

Purpose

To address sensor redundancy and single-dimension (either phase or pattern) in gait recognition, this study proposes a few-channel multi-information fusion framework to achieve concurrent gait pattern-phase recognition, balancing recognition accuracy, system simplicity, and user comfort.

Methods

Ten healthy males performed five gait patterns (level walking, stair ascent/descent, ramp ascent/descent). Lower limb surface electromyogram (sEMG), acceleration, and plantar pressure signals were collected. Gait cycles were split into five phases using plantar pressure signals, features were extracted from intra-phase sEMG and acceleration signals, selected via Maximum Relevance Minimum Redundancy (mRMR), and classified by Extreme Gradient Boosting (XGBoost) for gait recognition.

Results

Firstly, a study of five-class classification of gait phases for one single terrain using sensor data from one single muscle location showed that signals at the medial calf (gastrocnemius) or thigh (rectus femoris) had better classification performance with an average accuracy of 91.58%. Secondly, when the five gait phases from five different terrains were combined into a 25-class classification task, the results of fusing the two-channel signals from the calf and the two-channel signals at the thigh were analyzed separately. With fewer channels and features, the classification accuracy reached 88.5%, covering 25 classes.

Conclusion

Considering that lower leg amputations constitute a significant proportion of all major lower limb amputations, we suggest that the fusion of signals from the rectus femoris and semitendinosus positions as the signal sources should be selected for intelligent prosthesis control.