地面反作用力
惯性测量装置
运动学
计算机科学
均方误差
运动捕捉
人工智能
运动(物理)
力矩(物理)
流离失所(心理学)
步态
可视化
加速度计
计算机视觉
模拟
控制理论(社会学)
数学
物理医学与康复
物理
控制(管理)
经典力学
统计
医学
操作系统
心理学
心理治疗师
作者
Sei-ichi Sakamoto,Yonatan Hutabarat,Dai Owaki,Mitsuhiro Hayashibe
标识
DOI:10.34133/cbsystems.0016
摘要
Motion prediction based on kinematic information such as body segment displacement and joint angle has been widely studied. Because motions originate from forces, it is beneficial to estimate dynamic information, such as the ground reaction force (GRF), in addition to kinematic information for advanced motion prediction. In this study, we proposed a method to estimate GRF and ground reaction moment (GRM) from electromyography (EMG) in combination with and without an inertial measurement unit (IMU) sensor using a machine learning technique. A long short-term memory network, which is suitable for processing long time-span data, was constructed with EMG and IMU as input data to estimate GRF during posture control and stepping motion. The results demonstrate that the proposed method can provide the GRF estimation with a root mean square error (RMSE) of 8.22 ± 0.97% (mean ± SE) for the posture control motion and 11.17 ± 2.16% (mean ± SE) for the stepping motion. We could confirm that EMG input is essential especially when we need to predict both GRF and GRM with limited numbers of sensors attached under knees. In addition, we developed a GRF visualization system integrated with ongoing motion in a Unity environment. This system enabled the visualization of the GRF vector in 3-dimensional space and provides predictive motion direction based on the estimated GRF, which can be useful for human motion prediction with portable sensors.
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