Sun Mingxu, Li Yichen, Gao Rui, Yu Jingwen, Xu Yuan
School of Electrical Engineering, University of Jinan, Jinan 250022, China.
Jinan Key Laboratory of Rehabilitation and Evaluation of Motor Dysfunction, Jinan 250022, China.
Micromachines (Basel). 2024 Mar 26;15(4):440. doi: 10.3390/mi15040440.
To obtain precise positional information, in this study, we propose an adaptive expectation-maximization (EM)-based Kalman filter (KF)/finite impulse response (FIR) integrated filter for inertial navigation system (INS)-based posture capture of human upper limbs. Initially, a data fusion model for wrist and elbow position is developed. Subsequently, the distance is utilized to evaluate the performance of the filter. The integrated filter employs the EM-based KF to enhance noise estimation accuracy when the performance of KF declines. Conversely, upon deterioration in the performance of the EM-based KF, which is evaluated using the distance, the FIR filter is employed to maintain the effectiveness of the data fusion filter. This research utilizes the proposed EM-based KF/FIR integrated filter to ascertain wrist and elbow positions. The empirical results demonstrate the proficiency of the proposed approach in estimating these positions, thereby overcoming the challenge and highlighting its inherent effectiveness.
为了获得精确的位置信息，在本研究中，我们提出了一种基于自适应期望最大化（EM）的卡尔曼滤波器（KF）/有限脉冲响应（FIR）集成滤波器，用于基于惯性导航系统（INS）的人体上肢姿态捕捉。首先，开发了手腕和肘部位置的数据融合模型。随后，利用该距离来评估滤波器的性能。当KF性能下降时，集成滤波器采用基于EM的KF来提高噪声估计精度。相反，当基于EM的KF性能恶化（使用该距离评估）时，采用FIR滤波器来维持数据融合滤波器的有效性。本研究利用所提出的基于EM的KF/FIR集成滤波器来确定手腕和肘部的位置。实证结果证明了所提出方法在估计这些位置方面的熟练程度，从而克服了挑战并突出了其固有的有效性。
