Tremor Signal Analysis for Parkinson’s Disease Detection Using Leap Motion Device
Tremor is an involuntary rhythmic movement observed in people with Parkinson’s disease (PD), specifically, hand tremor is a measurement for diagnosing this disease. In this paper, we use hand positions acquired by Leap Motion device for statistical analysis of hand tremor based on the sum and difference of histograms (SDH). Tremor is measured using only one coordinate of the center palm during predefined exercises performed by volunteers at Hospital. In addition, the statistical features obtained with SDH are used to classify tremor signal as with PD or not. Experimental results show that the classification is independent of the hand used during tests, achieving \(98\%\) of accuracy for our proposed approach using different supervised machine learning classifiers. Additionally, we compare our result with others classifiers proposed in the literature.
KeywordsTremor analysis Parkinson detection SDH method Classification
Authors gratefully acknowledge all the volunteers at Edmonton Kaye Alberta Clinic, Canada, the Research and Postgraduate studies Support Program (DAIP) by the Universidad de Guanajuato and the Universidad Autónoma “Benito Juárez ”de Oaxaca.
- 1.Alam, M.N., Johnson, B., Gendreau, J., Tavakolian, K., Combs, C., Fazel-Rezai, R.: Tremor quantification of Parkinson’s disease - a pilot study. In: 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0755–0759, May 2016Google Scholar
- 3.Butt, A.H., Rovini, E., Dolciotti, C., Bongioanni, P., Petris, G.D., Cavallo, F.: Leap motion evaluation for assessment of upper limb motor skills in Parkinson’s disease. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 116–121, July 2017Google Scholar
- 4.Chen, K.H., Lin, P.C., Chen, Y.J., Yang, B.S., Lin, C.H.: Development of method for quantifying essential tremor using a small optical device. J. Neurosci. Methods 266, 78–83 (2016). http://www.sciencedirect.com/science/article/pii/S0165027016300206CrossRefGoogle Scholar
- 6.Ibarra-Manzano, M.A., Devy, M., Boizard, J.L.: Real-time classification based on color and texture attributes on an FPGA-based architecture. In: 2010 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 250–257, Oct 2010Google Scholar
- 7.Jilbab, A., Benba, A., Hammouch, A.: Quantification system of Parkinson’s disease. Int. J. Speech Technol. 40, 1–8 (2017)Google Scholar
- 8.Johnson, M.J.: Detection of Parkinson disease rest tremor. Master thesis, Washington University, Department of Electrical and Systems Engineering. School of Engineering and Applied Science (2014)Google Scholar
- 10.Lugo, G., Ibarra-Manzano, M., Ba, F., Cheng, I.: Virtual reality and hand tracking system as a medical tool to evaluate patients with Parkinson’s. In: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2017, pp. 405–408. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3154862.3154924
- 12.Münzenmayer, C., Wilharm, S., Hornegger, J., Wittenberg, T.: Illumination invariant color texture analysis based on sum- and difference-histograms. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 17–24. Springer, Heidelberg (2005). https://doi.org/10.1007/11550518_3CrossRefGoogle Scholar
- 14.Perumal, S.V., Sankar, R.: Gait and tremor assessment for patients with parkinson’s disease using wearable sensors. ICT Express 2(4), 168–174 (2016). http://www.sciencedirect.com/science/article/pii/S2405959516301382. special Issue on Emerging Technologies for Medical Diagnostics
- 15.van der Stouwe, A., et al.: How typical are “typical" tremor characteristics? Sensitivity and specificity of five tremor phenomena. Parkinsonism Relat. Disord. 30, 23–28 (2016). http://www.sciencedirect.com/science/article/pii/S1353802016302280
- 18.Vaillancourt, D.E., Newell, K.M.: The dynamics of resting and postural tremor in Parkinson’s disease. Clin. Neurophysiol. 111(11), 2046–2056 (2000). http://www.sciencedirect.com/science/article/pii/S1388245700004673CrossRefGoogle Scholar
- 19.Villalon-Hernandez, M.-T., Almanza-Ojeda, D.-L., Ibarra-Manzano, M.-A.: Color-texture image analysis for automatic failure detection in tiles. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds.) MCPR 2017. LNCS, vol. 10267, pp. 159–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59226-8_16CrossRefGoogle Scholar