| 高经纬,马超,姚杰,王少红.基于机器学习的人体步态检测智能识别算法研究[J].电子测量与仪器学报,2021,35(3):49-55 |
| 基于机器学习的人体步态检测智能识别算法研究 |
| Research on intelligent recognition algorithm of human gait detection based on machine learning |
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| DOI： |
| 中文关键词: 外骨骼 机器学习 决策树 步态分析 智能算法 |
| 英文关键词:exoskeleton machine learning decision tree gait analysis intelligent algorithm |
| 基金项目:促进高校内涵发展-学科建设专项资助项目（5112011015）、北京市属高校高水平创新团队建设计划项目（IDHT20180513）、现代测控技术教育部重点实验室开放课题项目（KF20181123206）资助 |
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| Author | Institution |
| Gao Jingwei | 1.Key Laboratory of Modern Measurement and Control Technology, Ministry of Education,Beijing Information Science and Technology University, Beijing 100192,China; |
| Ma Chao | 1.Key Laboratory of Modern Measurement and Control Technology, Ministry of Education,Beijing Information Science and Technology University, Beijing 100192,China; |
| Yao Jie | 2.School of Biological Science and Medical Engineering，Beihang University，Beijing 100191，China |
| Wang Shaohong | 1.Key Laboratory of Modern Measurement and Control Technology, Ministry of Education,Beijing Information Science and Technology University, Beijing 100192,China; |
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| 摘要点击次数: 1147 |
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| 中文摘要: |
| 为实现快速步态状态判断，以更好地对下肢外骨骼进行高精度的步态识别和控制，进行了基于可穿戴惯性测量装置检测人体姿态变化的算法研究。通过对人体下肢的跌倒、转弯、蹲坐与起立等非周期性步态变化活动进行测算试验，获得了受试者实验过程中身体角度、下肢关节角速度和加速度变化等数据，随后应用随机森林等4种机器学习经典分类算法对受试者进行了活动识别对比分析，结果表明,决策树监督学习算法相对于其他算法，能够快速、准确地检测并判断出人体非周期性变化中的多种活动状态，历次识别精度均可达到99%以上,为可穿戴智能装备的开发与应用提供理论基础。 |
| 英文摘要: |
| In order to achieve rapid gait state judgment and analysis to better perform high precision gait recognition and control of the exoskeleton of the lower limbs, the algorithm research based on wearable inertial measurement device to detect human body posture change is carried out. Through the measurement experiment of non periodical gait changes such as falling, turning, squatting and standing up of the lower limbs of the human body, data of the subjects’ body angle, lower limb joint angular velocity and acceleration changes during the experiment were obtained, and then random forests were applied. Four classic classification algorithms for machine learning have performed a comparative analysis of activity recognition on subjects. The results show that compared with other algorithms, the decision tree supervised learning algorithm can quickly and accurately detect and judge a variety of non periodic changes in the human body in the active state, the previous recognition accuracy can reach more than 99%. Research can provide a theoretical basis for the development and application of wearable smart equipment. |
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