School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Department of System Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Sensors (Basel). 2017 Nov 22;17(11):2698. doi: 10.3390/s17112698.
Climbing and descending stairs are demanding daily activities, and the monitoring of them may reveal the presence of musculoskeletal diseases at an early stage. A markerless system is needed to monitor such stair walking activity without mentally or physically disturbing the subject. Microsoft Kinect v2 has been used for gait monitoring, as it provides a markerless skeleton tracking function. However, few studies have used this device for stair walking monitoring, and the accuracy of its skeleton tracking function during stair walking has not been evaluated. Moreover, skeleton tracking is not likely to be suitable for estimating body joints during stair walking, as the form of the body is different from what it is when it walks on level surfaces. In this study, a new method of estimating the 3D position of the knee joint was devised that uses the depth data of Kinect v2. The accuracy of this method was compared with that of the skeleton tracking function of Kinect v2 by simultaneously measuring subjects with a 3D motion capture system. The depth data method was found to be more accurate than skeleton tracking. The mean error of the 3D Euclidian distance of the depth data method was 43.2 ± 27.5 mm, while that of the skeleton tracking was 50.4 ± 23.9 mm. This method indicates the possibility of stair walking monitoring for the early discovery of musculoskeletal diseases.
上下楼梯是日常活动中比较费力的动作，对其进行监测可能有助于在早期发现肌肉骨骼疾病。需要有一种无需在精神上或身体上干扰受试者的无标记系统来监测这种楼梯行走活动。微软 Kinect v2 已被用于步态监测，因为它提供了无标记骨骼跟踪功能。然而，很少有研究使用该设备来监测楼梯行走，并且尚未评估其在楼梯行走期间的骨骼跟踪功能的准确性。此外，骨骼跟踪可能不适合估计楼梯行走时的身体关节，因为身体的形状与在水平表面行走时不同。在这项研究中，设计了一种使用 Kinect v2 的深度数据来估计膝关节 3D 位置的新方法。通过同时使用 3D 运动捕捉系统测量受试者，比较了该方法的准确性和 Kinect v2 的骨骼跟踪功能。结果发现，深度数据方法比骨骼跟踪方法更准确。深度数据方法的 3D 欧几里得距离的平均误差为 43.2±27.5mm，而骨骼跟踪的平均误差为 50.4±23.9mm。该方法表明，有可能通过监测楼梯行走来早期发现肌肉骨骼疾病。
