Abstract
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.
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Acknowledgments
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.
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Vivar-Estudillo, G., Ibarra-Manzano, MA., Almanza-Ojeda, DL. (2018). Tremor Signal Analysis for Parkinson’s Disease Detection Using Leap Motion Device. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_26
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DOI: https://doi.org/10.1007/978-3-030-04491-6_26
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