Classification of Involuntary Hand Movements

Aki Härmä

2015

Abstract

Involuntary movements of arms and legs reflect neural and metabolic processes in the human body. In this paper the focus is on the properties of physiological tremor, shivering, and tremors caused by physical fatigue measured in fingers of a subject. Three different signal modeling paradigms are compared in the paper using accelerometer data. It is first demonstrated that the data can be modeled as a nearly stationary low-order AR process. Next, it is shown that the different data types can be classified using long-term feature distributions in a naive Bayes classifier. Finally, a comparable performance is obtained when the signal is modeled as a Markov process emitting small prototypical movements or jerks.

References

  1. Ackmann, J. J., Sances, A., Larson, S. J., and Baker, J. B. (1977). Quantitative Evaluation of Long-Term Parkinson Tremor. Biomedical Engineering, IEEE Transactions on, BME-24(1):49-56.
  2. Akamatsu, N., Hannaford, B., and Stark, L. (1988). An intrinsic mechanism for the oscillatory contraction of muscle. In Engineering in Medicine and Biology Society, 1988. Proceedings of the Annual International Conference of the IEEE, pages 1730-1731 vol.4. IEEE.
  3. Becker, B. C., Tummala, H., and Riviere, C. N. (2008). Autoregressive modeling of physiological tremor under microsurgical conditions. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 1948- 1951. IEEE.
  4. Ebenbichler, G. R., Kollmitzer, J., Erim, Z., Löscher, W. N., Kerschan, K., Posch, M., Nowotny, T., Kranzl, A., Wöber, C., and Bochdansky, T. (2000). Loaddependence of fatigue related changes in tremor around 10 Hz. Clinical neurophysiology, 111(1):106- 111.
  5. Flash, T. and Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. The Journal of Neuroscience, 5(7):1688- 1703.
  6. Gantert, C., Honerkamp, J., and Timmer, J. (1992). Analyzing the dynamics of hand tremor time series. Biological Cybernetics, 66:479-484.
  7. Grenier, Y. (1983). Time-dependent ARMA modeling of nonstationary signals. Acoustics, Speech and Signal Processing, IEEE Transactions on, 31(4):899-911.
  8. Jakubowski, J., Kwiatos, K., Chwaleba, A., and Osowski, S. (2002). Higher order statistics and neural network for tremor recognition. IEEE transactions on bio-medical engineering, 49(2):152-159.
  9. Kucukelbir, A., Kushki, A., and Plataniotis, K. N. (2009). A new stochastic estimator for tremor frequency tracking. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pages 421-424. IEEE.
  10. Morrison, S. F. and Nakamura, K. (2011). Central neural pathways for thermoregulation. Frontiers in bioscience : a journal and virtual library, 16:74-104.
  11. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). The MIT Press.
  12. Palmes, P., Ang, W. T., Widjaja, F., Tan, L. C. S., and Au, W. L. (2010). Pattern Mining of Multichannel sEMG for Tremor Classification. Biomedical Engineering, IEEE Transactions on, 57(12):2795-2805.
  13. Sung, M., DeVaul, R., Jimenez, S., Gips, J., and Pentland, A. S. (2004). Shiver Motion and Core Body Temperature Classification for Wearable Soldier Health Monitoring Systems. In Proceedings of the Eighth International Symposium on Wearable Computers, ISWC 7804, pages 192-193, Washington, DC, USA. IEEE Computer Society.
  14. Vinjamuri, R., Crammond, D. J., Kondziolka, D., Lee, H.- N., and Mao, Z.-H. (2009). Extraction of Sources of Tremor in Hand Movements of Patients With Movement Disorders. Information Technology in Biomedicine, IEEE Transactions on, 13(1):49-56.
  15. Watts, R. L., Standaert, D. G., and Obeso, J. (2011). Movement Disorders, Third Edition. McGraw-Hill Professional, 3 edition.
  16. Wyatt, R. H. (1968). A Study of Power Spectra Analysis of Normal Finger Tremors. Biomedical Engineering, IEEE Transactions on, BME-15(1):33-45.
  17. Yao, P., Zhang, D., and Hayashibe, M. (2012). Simulation of tremor on 3-dimentional musculoskeletal model of wrist joint and experimental verification ? In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pages 4823-4826. IEEE.
  18. Zhang, D., Zhu, X., and Poignet, P. (2009). Coupling of central and peripheral mechanism on tremor. In Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on, pages 649-652. IEEE.
  19. Zhang, J. and Chu, F. (2005). Real-time modeling and prediction of physiological hand tremor. In Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on, volume 5, pages v/645-v/648 Vol. 5. IEEE.
Download


Paper Citation


in Harvard Style

Härmä A. (2015). Classification of Involuntary Hand Movements . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 312-317. DOI: 10.5220/0005280503120317


in Bibtex Style

@conference{biosignals15,
author={Aki Härmä},
title={Classification of Involuntary Hand Movements},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={312-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005280503120317},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Classification of Involuntary Hand Movements
SN - 978-989-758-069-7
AU - Härmä A.
PY - 2015
SP - 312
EP - 317
DO - 10.5220/0005280503120317