Sinha Sanjana, Bhowmick Brojeshwar, Sinha Aniruddha, Das Abhijit
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3864-3867. doi: 10.1109/EMBC.2017.8037700.
Kinect as an effective tool for clinical assessment and rehabilitation, suffers from drawbacks of lower accuracy of measuring human body kinematic data when compared to clinical gold standard motion capture devices. The accuracy of time-varying 3D locations of a fixed number of body joints obtained from Kinect skeletal tracking utility is affected by the presence of noise and precision limits of the Kinect depth sensor. In this paper, a framework for improving accuracy of Kinect skeletal tracking is proposed, that uses a set of parametric models to represent and track the human body. Each of the models represents the 3D geometric properties of a body segment connecting two adjacent joints. The temporal trajectories of the joints are recovered via particle filter-based motion tracking of each model. The proposed method was evaluated on Active Range of Motion exercises by 7 healthy subjects. The joint motion trajectories obtained using the proposed framework exhibit a greater motion smoothness (by 36%) along with reduced coefficient of variation of radius (by 34%), and lower value of root-mean-squared-error (by 53%), when compared to Kinect joint trajectories. This indicates an improvement in accuracy of joint motion trajectories using Kinect device, rendering it more suitable for clinical assessment and rehabilitation.
与临床金标准运动捕捉设备相比，作为临床评估和康复有效工具的Kinect，在测量人体运动学数据时存在精度较低的缺点。从Kinect骨骼跟踪实用程序获得的固定数量身体关节的时变3D位置的精度，受到噪声的存在以及Kinect深度传感器精度限制的影响。本文提出了一种提高Kinect骨骼跟踪精度的框架，该框架使用一组参数模型来表示和跟踪人体。每个模型表示连接两个相邻关节的身体节段的3D几何属性。通过基于粒子滤波器的每个模型的运动跟踪来恢复关节的时间轨迹。7名健康受试者对主动运动范围练习评估了所提出的方法。与Kinect关节轨迹相比，使用所提出框架获得的关节运动轨迹表现出更大的运动平滑度（提高36%），同时半径变化系数降低（降低34%），均方根误差值更低（降低53%）。这表明使用Kinect设备的关节运动轨迹精度有所提高，使其更适合临床评估和康复。
