Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data

Julia Richter, Christian Wiede, Bharat Shinde, Gangolf Hirtz

Abstract

Preventive and therapeutic measures can contribute to maintain or to regain physical abilities. In Germany, the growing number of elderly people is posing serious challenges for the therapeutic sector. Therefore, the objective that has been pursued in recent research is to assist patients during their medical training by reproducing therapists' feedback. Extant systems have been limited to feedback that is based on the evaluation of only the similarity between a pre-recorded reference and the currently performed motion. To date, very little is known about feedback generation that exceeds such similarity evaluations. Moreover, current systems require a personalised, pre-recorded reference for each patient in order to compare the reference against the motion performed during the exercise and to generate feedback. The aim of this study is to develop and evaluate an error classification algorithm for therapy exercises using Incremental Dynamic Time Warping and 3-D skeleton joint information. Furthermore, a normalisation method that allows the utilisation of non-personalised references has been investigated. In our experiments, we were able to successfully identify errors, even for non-personalised reference data, by using normalised hierarchical coordinates.

References

  1. Huang, T.-C., Cheng, Y.-C., and Chiang, C.-C. (2013). Automatic Dancing Assessment Using Kinect. In Advances in Intelligent Systems and ApplicationsVolume 2, pages 511-520. Springer.
  2. Khan, N. M., Lin, S., Guan, L., and Guo, B. (2014). A visual evaluation framework for in-home physical rehabilitation. In Multimedia (ISM), 2014 IEEE International Symposium on Multimedia, pages 237-240. IEEE.
  3. Lin, T.-Y., Hsieh, C.-H., and Lee, J.-D. (2013). A kinectbased 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.
  4. Muneesawang, P., Khan, N. M., Kyan, M., Elder, R. B., Dong, N., Sun, G., Li, H., Zhong, L., and Guan, L. (2015). A machine intelligence approach to virtual ballet training. IEEE MultiMedia, 22(4):80-92.
  5. Richter, J., Wiede, C., Apitzsch, A., Nitzsche, N., Lösch, Christiane amd Weigert, M., Kronfeld, T., Weisleder, S., and Hirtz, G. (2016). Assisted Motion Control in Therapy Environments Using Smart Sensor Technology: Challenges and Opportunities. In Proceedings of Zukunft Lebensräume Kongress 2016, pages 90-102.
  6. Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al. (2013). Efficient human pose estimation from single depth images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(12):2821-2840.
  7. Su, C.-J., Chiang, C.-Y., and Huang, J.-Y. (2014). Kinectenabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22:652-666.
  8. Tak, Y.-S., Rho, S., and Hwang, E. (2011). Motion Sequence-Based Human Abnormality Detection Scheme for Smart Spaces. Wireless Personal Communications, 60(3):507-519.
  9. Tormene, P., Giorgino, T., Quaglini, S., and Stefanelli, M. (2009). Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artificial intelligence in medicine, 45(1):11-34.
  10. Yurtman, A. and Barshan, B. (2013). Detection and evaluation of physical therapy exercises by dynamic time warping using wearable motion sensor units. In Information Sciences and Systems 2013, pages 305-314. Springer.
Download


Paper Citation


in Harvard Style

Richter J., Wiede C., Shinde B. and Hirtz G. (2017). Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 281-288. DOI: 10.5220/0006108002810288


in Bibtex Style

@conference{icpram17,
author={Julia Richter and Christian Wiede and Bharat Shinde and Gangolf Hirtz},
title={Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006108002810288},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data
SN - 978-989-758-222-6
AU - Richter J.
AU - Wiede C.
AU - Shinde B.
AU - Hirtz G.
PY - 2017
SP - 281
EP - 288
DO - 10.5220/0006108002810288