基于随机森林算法的背包负重行走疲劳检测研究
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1.上海体育大学;2.复旦大学

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Research on fatigue detection of walking with backpack load based on random forest algorithm
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1.Shanghai University of Sport;2.Fudan University

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    摘要:

    目的:使用可穿戴惯性传感器(IMU)技术和随机森林(RF)算法检测长距离背包负重行走的疲劳水平,探究负重行走疲劳检测的可行性和最佳IMU组合方案。方法:招募30名健康男性大学生进行长距离背包负重行走。应用Xsens MVN Link惯性运动捕捉系统和Borg-RPE疲劳量表采集负重行走的运动学数据和主观疲劳值,将疲劳分为无疲劳、中度疲劳和重度疲劳3个等级。提取原始数据,进行步态分割、数据筛选和特征提取,利用RF模型对样本特征进行机器学习,最后计算准确率、混淆矩阵和AUC值对不同IMU组合的检测效果加以评定。结果:1个右股骨IMU准确率达到82.55%,5个IMU组合的准确率最高,为87.94%。在多个IMU的组合中,至少包含一个上半身IMU,且左侧肢体的IMU多于右侧。同时,RF模型对负重步行的疲劳检测具有较高的水平,使用4个IMU时,三级疲劳的AUC值分别为0.99、0.97和0.99。结论:IMU技术和RF算法在背包负重行走的三级疲劳检测任务中具有较高的准确率和分类能力,在实际应用中推荐采用1-5个IMU,上半身IMU和下肢IMU结合的配置方案。

    Abstract:

    Objective: Wearable inertial sensor (IMU) technology and Random forest (RF) algorithm were used to detect the fatigue level of long-distance backpack walking, and the feasibility and optimal IMU combination scheme were explored. Methods: Thirty healthy male college students were recruited to carry out long-distance backpack walking. Xsens MVN Link inertial motion capture system and Borg-RPE fatigue scale were used to collect kinematic data and subjective fatigue values of load-bearing walking, and fatigue was divided into 3 levels: no fatigue, moderate fatigue and severe fatigue. The original data were extracted, gait segmentation, data screening and feature extraction were carried out, and RF model was used for machine learning of sample features. Finally, the accuracy rate, confusion matrix and AUC value were calculated to evaluate the detection effect of different IMU combinations. Results: The accuracy of one right femur IMU was 82.55%, and the accuracy of five IMU combinations was 87.94%. In a combination of IMUs, at least one upper body IMU is included, and the left limb has more IMU than the right. The RF model had a higher level of fatigue detection for load-bearing walking, and the AUC values of level 3 fatigue were 0.99, 0.97 and 0.99, respectively, when four IMUs were used. Conclusions: IMU technology and RF algorithm have high accuracy and classification ability in the third-level fatigue detection task of walking with backpack load. In practical application, it is recommended to use 1-5 IMUs, and the combination of upper body IMU and lower limb IMU configuration scheme.

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  • 收稿日期:2024-05-14
  • 最后修改日期:2024-07-08
  • 录用日期:2024-07-16
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