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Kinect-Based In-Home Exercise System for Lymphatic Health and Lymphedema Intervention

机译：基于Kinect的家庭锻炼系统，用于淋巴健康和淋巴水肿干预

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摘要

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 thesis 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 a 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.
机译：使用Kinect传感器监视进行干预或康复锻炼的患者并向其提供反馈是医疗保健领域的一种新兴趋势。但是，由Kinect传感器测量的关节位置通常不可靠，尤其是对于身体其他部位所遮挡的关节而言。而且，即使进行相同的锻炼，用户的运动序列也有很大差异，并且在时间上没有对齐，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锻炼。为了避免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。我们在训练阶段同时使用Kinect传感器和运动捕获（MOCAP）系统同时捕获关节位置，并训练高斯过程回归模型以将嘈杂的Kinect测量值映射到更准确的MOCAP测量值。对于序列比对问题，我们开发了一种梯度加权动态时间规整方法，该方法可以自动从原始用户的运动序列中识别不同子序列的端点，并且还可以在时间上比对来自多个演员的子序列。在实时锻炼期间，系统将相同的比对算法应用于实时捕获的Kinect序列，将其划分为子序列，然后将每个子序列与其对应的参考子序列进行比较，并根据比较结果生成反馈给用户。我们的结果表明，所提出的去噪算法所进行的去噪Kinect测量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运动中以很小的延迟精确地检测每个子序列的结尾。这些方法已集成到一个原型系统中，以指导有乳腺癌相关淋巴水肿风险的患者进行一系列淋巴练习。该系统可以向执行实时运动的患者提供相关的反馈。

著录项

  • 作者

    Chiang, An-Ti.;

  • 作者单位

    New York University Tandon School of Engineering.;

  • 授予单位 New York University Tandon School of Engineering.;
  • 学科 Electrical engineering.;Environmental geology.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 84 p.
  • 总页数 84
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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