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

October 31, 2018

An-Ti ChiangQi ChenYao WangMei R. Fu

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

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

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.

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