基于Azure Kinect运动捕捉的下肢关节力学和地面反作用力分析
Lower limb joint contact forces and ground reaction forces analysis based on Azure Kinect motion capture
彭迎虎 1王琳 2陈瑱贤 3党晓栋 3陈飞 4李光林1
作者信息
- 1. 中国科学院深圳先进技术研究院人机智能协同系统重点实验室(广东深圳 518055)
- 2. 中国科学院深圳先进技术研究院人机智能协同系统重点实验室(广东深圳 518055);深圳市下肢康复智能辅具工程研究中心(广东深圳 518055)
- 3. 长安大学工程机械学院(西安 710064)
- 4. 香港理工大学工学院生物医学工程系(中国香港 999077)
- 折叠
摘要
传统的步态分析系统往往操作复杂、便携性差且设备成本高.本研究拟基于Azure Kinect深度视频数据,结合足地接触模型,建立基于Azure Kinect运动捕捉系统的下肢肌骨动力学分析流程.实验采集了 10名受试者的深度视频数据,通过预处理获取骨架结构,以此作为肌骨模型输入,计算得到下肢关节角度、关节接触力和地面反作用力,并将其计算结果与传统的Vicon系统获取的运动学和动力学数据进行对比.所计算的下肢关节力和地面反作用力除以每位受试者体重,进行归一化处理.下肢关节角度曲线与Vicon得到的结果强相关(ρ平均值为0.78～0.92),但均方根误差高达5.66°.在下肢关节力预测方面,Azure Kinect模型均方根误差平均值范围为0.44～0.68,而地面反作用力均方根误差平均值范围为0.01～0.09.研究表明,所建立的基于Azure Kinect的肌骨动力学模型能较好地预测下肢关节力和垂直地面反作用力,但在下肢关节角度预测方面还存在一定误差.
Abstract
Traditional gait analysis systems are typically complex to operate,lack portability,and involve high equipment costs.This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect.Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model,the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants.The in-depth videos were prepossessed to extract keypoint of the participants,which were then adopted as inputs for the musculo-skeletal model to compute lower limb joint angles,joint contact forces,and ground reaction forces.To validate the Azure Kinect computational model,the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system.The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight.The lower limb joint angle curves showed a strong correlation with Vicon results(mean ρ values:0.78～0.92)but with root mean square errors as high as 5.66°.For lower limb joint force prediction,the model exhibited root mean square errors ranging from 0.44 to 0.68,while ground reaction force root mean square errors ranged from 0.01 to 0.09.The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces,but some errors remain in predicting lower limb joint angles.
关键词
下肢关节力/Azure/Kinect运动捕捉/地面反作用力/肌骨多体动力学/步态Key words
Lower limb joint contact forces/Azure Kinect motion capture/Ground reaction forces/Musculoskeletal multibody dynamics/Gait引用本文复制引用
基金项目
国家自然科学基金(12302421)
广东省基础与应用基础研究基金(2022A1515110512)
广东省重点研发计划(2022A0505090007)
深圳市可持续发展科技专项(KCXFZ20230731093501003)
深圳市战略性新兴产业专项(XMHT20230115002)
出版年
2024
NETL
NSTL
万方数据