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Open AccessArticle

Classification of Essential Tremor and Parkinson’s Tremor Based on a Low-Power Wearable Device

1
Department of Engineering and Applied Sciences, University of Bergamo, Viale Marconi 5, 24044 Dalmine (BG), Italy
2
Department of Neuroscience, Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
3
PhD Program in Neuroscience, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1695; https://doi.org/10.3390/electronics9101695
Received: 31 August 2020 / Revised: 10 October 2020 / Accepted: 12 October 2020 / Published: 15 October 2020
(This article belongs to the Section Bioelectronics)
Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians. View Full-Text
Keywords: movement disorders; essential tremor; Parkinson’s disease; Parkinsonian tremor; wearable device movement disorders; essential tremor; Parkinson’s disease; Parkinsonian tremor; wearable device
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Locatelli, P.; Alimonti, D.; Traversi, G.; Re, V. Classification of Essential Tremor and Parkinson’s Tremor Based on a Low-Power Wearable Device. Electronics 2020, 9, 1695.

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