Hicks Hailey N, Chen Howard, Harper Sara A
Industrial & Systems Engineering and Engineering Management Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA.
Kinesiology Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA.
Sensors (Basel). 2025 Jul 29;25(15):4680. doi: 10.3390/s25154680.
This study aimed to create and evaluate an optimization-based sensor fusion algorithm that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to provide a more efficient and reliable gap-filling process for OMC measurements to be used for future research. The proposed algorithm takes the first and last frame of OMC data and fills the rest with gyroscope data from the IMC. The algorithm was validated using data from twelve participants who performed a hand cycling task with an inertial measurement unit (IMU) placed on their hand, forearm, and upper arm. The OMC tracked a cluster of reflective markers that were placed on top of each IMU. The proposed algorithm was evaluated with simulated gaps of up to five minutes. Average total root-mean-square errors (RMSE) of <1.8° across a 5 min duration were observed for all sensor placements for the cyclic upper limb motion pattern used in this study. The results demonstrated that the fusion of these two sensing modalities is feasible and shines light on the possibility of more field-based studies for human motion analysis.
本研究旨在创建并评估一种基于优化的传感器融合算法，该算法结合光学运动捕捉（OMC）和惯性运动捕捉（IMC）测量，为OMC测量提供更高效、可靠的间隙填充过程，以供未来研究使用。所提出的算法采用OMC数据的第一帧和最后一帧，并用IMC的陀螺仪数据填充其余部分。使用来自12名参与者的数据对该算法进行了验证，这些参与者在手、前臂和上臂佩戴惯性测量单元（IMU）执行手部循环任务。OMC跟踪放置在每个IMU顶部的一组反光标记。所提出的算法在长达五分钟的模拟间隙下进行了评估。对于本研究中使用的循环上肢运动模式，所有传感器放置在5分钟持续时间内的平均总均方根误差（RMSE）<1.8°。结果表明，这两种传感方式的融合是可行的，并为更多基于现场的人体运动分析研究提供了可能性。
