Chiang An-Ti, Chen Qi, Wang Yao, Fu Mei R
Department of Electrical and Computer EngineeringNYU Tandon School of EngineeringBrooklynNY11201USA.
NYU Rory Meyers College of NursingNew YorkNY10010USA.
IEEE J Transl Eng Health Med. 2018 Oct 12;6:4100313. doi: 10.1109/JTEHM.2018.2859992. eCollection 2018.
Using Kinect sensors to monitor and provide feedback to patients performing intervention or rehabilitation exercises is an upcoming trend in healthcare. However, the joint positions measured by the Kinect sensor are often unreliable, especially for joints that are occluded by other parts of the body. Also, users' motion sequences differ significantly even when doing the same exercise and are not temporally aligned, making the evaluation of the correctness of their movement challenging. This paper aims to develop a Kinect-based intervention system, which can guide the users to perform the exercises more effectively. To circumvent the unreliability of the Kinect measurements, we developed a denoising algorithm using a Gaussian Process regression model. We simultaneously capture the joint positions using both a Kinect sensor and a motion capture (MOCAP) system during a training stage and train a Gaussian process regression model to map the noisy Kinect measurements to the more accurate MOCAP measurements. For the sequences alignment issue, we develop a gradient-weighted dynamic time warping approach that can automatically recognize the endpoints of different subsequences from the original user's motion sequence, and furthermore temporally align the subsequences from multiple actors. During a live exercise session, the system applies the same alignment algorithm to a live-captured Kinect sequence to divide it into subsequences, and furthermore compare each subsequence with its corresponding reference subsequence, and generates feedback to the user based on the comparison results. Our results show that the denoised Kinect measurements by the proposed denoising algorithm are more accurate than several benchmark methods and the proposed temporal alignment approach can precisely detect the end of each subsequence in an exercise with very small amount of delay. These methods have been integrated into a prototype system for guiding patients with risks for breast-cancer related lymphedema to perform a set of lymphatic exercises. The system can provide relevant feedback to the patient performing an exercise in real time.
使用Kinect传感器来监测进行干预或康复训练的患者并提供反馈，是医疗保健领域即将兴起的一种趋势。然而，Kinect传感器测量的关节位置往往不可靠，尤其是对于被身体其他部位遮挡的关节。此外，即使进行相同的训练，用户的运动序列也存在显著差异，并且在时间上未对齐，这使得评估他们运动的正确性具有挑战性。本文旨在开发一种基于Kinect的干预系统，该系统可以指导用户更有效地进行训练。为了规避Kinect测量的不可靠性，我们使用高斯过程回归模型开发了一种去噪算法。在训练阶段，我们同时使用Kinect传感器和动作捕捉（MOCAP）系统来捕捉关节位置，并训练高斯过程回归模型，将有噪声的Kinect测量值映射到更准确的MOCAP测量值。对于序列对齐问题，我们开发了一种梯度加权动态时间规整方法，该方法可以自动识别原始用户运动序列中不同子序列的端点，并在时间上对齐多个参与者的子序列。在实时训练过程中，系统将相同的对齐算法应用于实时捕捉的Kinect序列，将其划分为子序列，进而将每个子序列与其相应的参考子序列进行比较，并根据比较结果向用户生成反馈。我们的结果表明，所提出的去噪算法去噪后的Kinect测量值比几种基准方法更准确，并且所提出的时间对齐方法可以在延迟非常小的情况下精确检测训练中每个子序列的结束。这些方法已被集成到一个原型系统中，用于指导有乳腺癌相关淋巴水肿风险的患者进行一组淋巴练习。该系统可以实时向进行训练的患者提供相关反馈。
