Abstract
Tremor is one of the cardinal symptoms of Parkinson’s disease. Currently, tremor severity is scored based on the Movement Disorders Society’s Unified Parkinson’s Disease Rating Scale, MDS-UPDRS, which is subjective and unreliable. Therefore, several studies have tried to measure tremor objectively using machine learning techniques. However, a limited number of studies have explored or compared medication state (ON or OFF) effect on objective measurement of tremor severity. Also, few studies have compared different types of wearable devices for tremor measurement. In this study, the medication state effect on tremor measurement is explored using different machine learning algorithms utilising different datasets that have been collected from different sensors. The results showed that the objective measurement of tremor severity is higher when patients are on medication using the Pebble smartwatch. The highest accuracy achieved was when patients were on medication and obtained \(80\%\) accuracy using Random Forest classifier, while the highest accuracy achieved when patients were off medication was \(77\%\) using Random Forest and Artificial Neural Network based on Multi-Layer Perceptron.
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AlMahadin, G., Lotfi, A., Carthy, M.M., Breedon, P. (2021). Parkinson’s Disease Tremor Severity Classification - A Comparison Between ON and OFF Medication State. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_29
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DOI: https://doi.org/10.1007/978-3-030-91100-3_29
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