Finger Motion Detection for Human Activities Recognition using Single sEMG Channel

Yang Qian, Ichiro Yamada, Shin'ichi Warisawa

2014

Abstract

Today’s aging population has recently become a significant problem, requiring a wearable health monitoring system for the elderly who are living alone. One of the focuses of this monitoring system is human activities recognition. We propose a wearable sensing method that is based on muscle’s crosstalk information that uses only one sEMG channel (a pair of electrodes) to recognize five basic finger motions (thumb flexion, index flexion, middle flexion, ring & little flexion, and rest position) related to daily human activities. In the first step, an inter-electrode distance (IED) experiment was conducted to define the suitable IED for crosstalk information collection. In this experiment’s recognition part, a conventional feature extraction method was adopted. The accuracy of each IED was compared and a suitable IED was defined (50 mm). In the second step, we propose two new features, the summit foot range (SFR) and summits number (SN), to represent the different patterns of finger motions’ sEMG signals and adopted the minimal Redundancy Maximal Relevance (mRMR) feature selection method to improve the accuracy. An accuracy of over 87% was achieved using the improved recognition methodology compared to 81.5% when using the conventional one.

References

  1. Al-Timemy, A., Bugmann, G., Escudero, J., and Outram, N., 2013. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE Journal of Biomedical and Health Informatics, Vol. 17, No. 3, pages 608-618.
  2. Cawley, G. C., 2006. Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In Proceedings of the International Joint Conference on Neural Networks (IJCNN'06), pages 1661-1668.
  3. Hargrove, L. J., Englehart, K., and Hudgins, B., 2007. A comparison of surface and intramuscular myoelectric signal classification. IEEE Transactions on Biomedical Engineering, Vol. 54, No. 5, pages 847-853.
  4. Hudgins, B., Parker, P., and Scott, R. N., 1993. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, Vol. 40, No. 1, pages 82-94.
  5. Ishikawa, K., Toda, M., Sakurazawa, S., Akita, J., Kondo, K., and Nakamura, Y., 2010. Finger motion classification using surface-electromyogram signals. In Proceedings of IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS), pages 37-42.
  6. Lee, D., and Lee, S., 2011. Vision-based finger action recognition by angle detection and contour analysis. ETRI Journal, Vol. 33, No. 3, pages 415-422.
  7. Moore, K. L., Arthur F. D., and Anne M. R. A., 2010. Clinically oriented anatomy. Wolters Kluwer Health, 6th edition, pages 748-750.
  8. Nagata, K., Ando, K., Magatani, K., and Yamada, M., 2007. Development of the hand motion recognition system based on surface EMG using suitable measurement channels for pattern recognition. In Proceedings of Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE. IEEE, pages 5214-5217.
  9. Peng, H., Long, F., and Ding, C., 2005. Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pages 1226-1238.
  10. Schaechter, J. D., Stokes, C., Connell, B. D., Perdue, K., and Bonmassar, G., 2006. Finger motion sensors for fMRI motor studies. Neuroimage, Vol. 31 No. 4, pages 1549-1559.
  11. Specht, D. F., 1990. Probabilistic neural networks. Neural Networks, Vol. 3, No. 1, pages 109-118.
  12. Tenore, F. V. G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, and R., Thakor, N. V., 2009. Decoding of individuated finger movements using surface electromyography. IEEE Transactions on Biomedical Engineering, Vol. 56, No. 5, pages 1427-1434.
  13. 1http://www.bioplux.com/home.
  14. 2http://www.mathworks.co.jp/jp/help/signal/ref/findpeaks. html?lang=en.
Download


Paper Citation


in Harvard Style

Qian Y., Yamada I. and Warisawa S. (2014). Finger Motion Detection for Human Activities Recognition using Single sEMG Channel . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 60-67. DOI: 10.5220/0004764700600067


in Bibtex Style

@conference{healthinf14,
author={Yang Qian and Ichiro Yamada and Shin'ichi Warisawa},
title={Finger Motion Detection for Human Activities Recognition using Single sEMG Channel},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={60-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004764700600067},
isbn={978-989-758-010-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Finger Motion Detection for Human Activities Recognition using Single sEMG Channel
SN - 978-989-758-010-9
AU - Qian Y.
AU - Yamada I.
AU - Warisawa S.
PY - 2014
SP - 60
EP - 67
DO - 10.5220/0004764700600067