Mangal Naveen Kumar, Tiwari Anil Kumar
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5769-5772. doi: 10.1109/EMBC44109.2020.9175492.
Body joint monitoring is essential for disorder diagnosis and assessment of treatment effectiveness. Microsoft Kinect v2 is a low-cost and markerless human motion-tracking RGB-D sensor that provides spatial locations of tracked skeletal joints in the form of 3D coordinates. Sometimes, body tracking of kinect v2 produces erratic 3D coordinates, which affects the real-time tracking performance of the sensor. A careful study of the literature suggests that skeletal tracking of kinect v2 needs further exploration. This work proposes a filter combined with the concept of body kinematics to remove noise and enhances the quality of 3D coordinates in body frame data. Also, it generates "Motion Signature" of the tracked joint, which shows movement pattern for kinematic analysis, and helpful in joint monitoring of Musculoskeletal Disorders (MSD). The clinically relevant anatomical movement was executed, to evaluate the performance of the proposed filter. We compared Range of Motion (RoM) values obtained from the proposed filter with the gold standard goniometry. Results indicate that RoM values from the proposed filter are in high correlation with the goniometry with an Intraclass Correlation Coefficient values ranging between 0.95 to 0.98 authenticating that it improves the skeletal joint tracking of kinect v2.
身体关节监测对于疾病诊断和治疗效果评估至关重要。微软Kinect v2是一种低成本且无需标记的人体运动跟踪RGB-D传感器，它以三维坐标的形式提供被跟踪骨骼关节的空间位置。有时，Kinect v2的人体跟踪会产生不稳定的三维坐标，这会影响该传感器的实时跟踪性能。对文献的仔细研究表明，Kinect v2的骨骼跟踪需要进一步探索。这项工作提出了一种结合身体运动学概念的滤波器，以去除噪声并提高身体框架数据中三维坐标的质量。此外，它还生成被跟踪关节的“运动特征”，展示运动模式以供运动学分析，有助于肌肉骨骼疾病（MSD）的关节监测。执行了临床相关的解剖运动，以评估所提出滤波器的性能。我们将从所提出滤波器获得的运动范围（RoM）值与金标准量角法进行了比较。结果表明，所提出滤波器的RoM值与量角法高度相关，组内相关系数值在0.95至0.98之间，证实它改善了Kinect v2的骨骼关节跟踪。
