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Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment (2404.09359v5)

Published 14 Apr 2024 in cs.CV, cs.AI, and cs.LG

Abstract: The application of machine-learning solutions to movement assessment from skeleton videos has attracted significant research attention in recent years. This advancement has made rehabilitation at home more accessible, utilizing movement assessment algorithms that can operate on affordable equipment for human pose detection and analysis from 2D or 3D videos. While the primary objective of automatic assessment tasks is to score movements, the automatic generation of feedback highlighting key movement issues has the potential to significantly enhance and accelerate the rehabilitation process. While numerous research works exist in the field of automatic movement assessment, only a handful address feedback generation. In this study, we propose terminology and criteria for the classification, evaluation, and comparison of feedback generation solutions. We discuss the challenges associated with each feedback generation approach and use our proposed criteria to classify existing solutions. To our knowledge, this is the first work that formulates feedback generation in skeletal movement assessment.

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References (47)
  1. Visual and human-interpretable feedback for assisting physical activity. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, pages 115–129. Springer, 2016.
  2. Video-based feedback for assisting physical activity. In 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2017.
  3. Lazier: A virtual fitness coach based on ai technology. In 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), pages 207–212. IEEE, 2022.
  4. A novel combination model of convolutional neural network and long short-term memory network for upper limb evaluation using kinect-based system. IEEE Access, 7:145227–145234, 2019.
  5. A hidden semi-markov model based approach for rehabilitation exercise assessment. Journal of biomedical informatics, 78:1–11, 2018.
  6. Abnormal gait detection with rgb-d devices using joint motion history features. In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), volume 7, pages 1–6. IEEE, 2015.
  7. Motor rehabilitation using kinect: a systematic review. Games for health journal, 4(2):123–135, 2015.
  8. A review of computer vision-based approaches for physical rehabilitation and assessment. Multimedia Systems, 28(1):209–239, 2022.
  9. Learning shape variations of motion trajectories for gait analysis. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 895–900. IEEE, 2016.
  10. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  11. Design of upper limb rehabilitation evaluation system based on deep learning. In Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), volume 12462, pages 695–700. SPIE, 2023.
  12. Data-driven human movement assessment. In Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2, pages 317–327. Springer, 2019.
  13. Towards an automated assessment of musculoskeletal insufficiencies. In Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 1, pages 251–261. Springer, 2020.
  14. 3d motion capture system for assessing patient motion during fugl-meyer stroke rehabilitation testing. IET Computer Vision, 12(7):963–975, 2018.
  15. Non-invasive motion analysis for stroke rehabilitation using off the shelf 3d sensors. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2018.
  16. Automatic and efficient fall risk assessment based on machine learning. Sensors, 22(4):1557, 2022.
  17. Assessing human motion during exercise using machine learning: A literature review. IEEE Access, 10:86874–86903, 2022.
  18. A tool for balance control training using muscle synergies and multimodal interfaces. BioMed research international, 2014, 2014.
  19. Tal Hakim. A comprehensive review of skeleton-based movement assessment methods. arXiv preprint arXiv:2007.10737, 2020.
  20. A-mal: Automatic motion assessment learning from properly performed motions in 3d skeleton videos. In Proceedings of the IEEE/CVF international conference on computer vision workshops, pages 0–0, 2019.
  21. A-mal: Automatic movement assessment learning from properly performed movements in 3d skeleton videos. arXiv preprint arXiv:1907.10004, 2019.
  22. Space-time representation of people based on 3d skeletal data: A review. Computer Vision and Image Understanding, 158:85–105, 2017.
  23. Real-time human movement retrieval and assessment with kinect sensor. IEEE transactions on cybernetics, 45(4):742–753, 2014.
  24. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality—a case study in squats and tai chi pushes. Computers & Graphics, 76:47–59, 2018.
  25. Feature extraction using an rnn autoencoder for skeleton-based abnormal gait recognition. IEEE Access, 8:19196–19207, 2020.
  26. Personalized monitoring in home healthcare: An assistive system for post hip replacement rehabilitation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1868–1877, 2023.
  27. Person identification from action styles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 84–92, 2015.
  28. Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pages 1366–1373. IEEE, 2020.
  29. Learning effective skeletal representations on rgb video for fine-grained human action quality assessment. Electronics, 9(4):568, 2020.
  30. Automated analysis and quantification of human mobility using a depth sensor. IEEE journal of biomedical and health informatics, 21(4):939–948, 2016.
  31. A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(2):468–477, 2020.
  32. A kinect-based system for physical rehabilitation: Utilizing tai chi exercises to improve movement disorders in patients with balance ability. In 2013 7th Asia Modelling Symposium, pages 149–153. IEEE, 2013.
  33. Predicting fall probability based on a validated balance scale. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 302–303, 2020.
  34. A comparative study of the clinical use of motion analysis from kinect skeleton data. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2808–2813. IEEE, 2017.
  35. Estimating skeleton-based gait abnormality index by sparse deep auto-encoder. In 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE), pages 311–315. IEEE, 2018.
  36. Skeleton-based gait index estimation with lstms. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), pages 468–473. IEEE, 2018.
  37. Joint angles similarities and hog2 for action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 465–470, 2013.
  38. An objective evaluation method for rehabilitation exergames. In 2018 IEEE Games, Entertainment, Media Conference (GEM), pages 28–34. IEEE, 2018.
  39. Online quality assessment of human movement from skeleton data. In British Machine Vision Conference, 2014.
  40. Human motion assessment in real time using recurrent self-organization. In 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN), pages 71–76. IEEE, 2016.
  41. Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, page 106835, 2023.
  42. Diffeomorphic temporal alignment nets. Advances in Neural Information Processing Systems, 32, 2019.
  43. Chuan-Jun Su. Personal rehabilitation exercise assistant with kinect and dynamic time warping. International Journal of Information and Education Technology, 3(4):448–454, 2013.
  44. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  45. Assessment of physical rehabilitation movements through dimensionality reduction and statistical modeling. Medical engineering & physics, 74:13–22, 2019.
  46. A dynamic time warping based algorithm to evaluate kinect-enabled home-based physical rehabilitation exercises for older people. Sensors, 19(13):2882, 2019.
  47. Rehabilitation exercise recognition and evaluation based on smart sensors with deep learning framework. IEEE Access, 8:77561–77571, 2020.

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