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Abstract：
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 Mahalanobis 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 Mahalanobis 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.
摘要：
为了获得精确的位置信息，在这项研究中，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）的人体上肢姿势捕获。最初，建立了腕部和肘部位置的数据融合模型。随后，马氏距离用于评估过滤器的性能。当KF的性能下降时，集成滤波器采用基于EM的KF来提高噪声估计精度。相反，在基于EM的KF的性能恶化时，这是使用马氏距离来评估的，FIR滤波器用于保持数据融合滤波器的有效性。本研究利用提出的基于EM的KF/FIR集成滤波器来确定手腕和肘部位置。实证结果证明了所提出的方法在估计这些头寸方面的熟练程度，从而克服挑战并突出其固有的有效性。
