Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises
<|reference_start|>Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises: Analyzing human motion is an active research area, with various applications. In this work, we focus on human motion analysis in the context of physical rehabilitation using a robot coach system. Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system, such as RGB and RGB-D cameras. As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data obtained from both RGB-D camera (Microsoft Kinect) and estimation from RGB videos (OpenPose and BlazePose algorithms). A Gaussian Mixture Model (GMM) is employed from position (and orientation) features, with performance metrics defined based on the log-likelihood values from GMM. The evaluation is performed on a medical database of clinical patients carrying out low back-pain rehabilitation exercises, previously coached by robot Poppy.<|reference_end|>
@article{marusic2024analyzing,
    title={Analyzing Data Efficiency and Performance of Machine Learning Algorithms
  for Assessing Low Back Pain Physical Rehabilitation Exercises},
    author={Aleksa Marusic and Louis Annabi and Sao Msi Nguyen and Adriana Tapus},
    journal={arXiv preprint arXiv:2408.02855},
    year={2024},
    doi={10.1145/3568294.3580153},
    archivePrefix={arXiv},
    eprint={2408.02855},
    primaryClass={cs.HC cs.CV}
}