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TitleAutomatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
Author(s)Sigcha, Luis
Pavón, Ignacio
Costa, Nélson
Costa, Susana
Gago, Miguel
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
Keywordsmachine learning
wearable sensors
resting tremor
deep learning
convolutional neural networks
Parkinson's disease
Issue date2021
PublisherMultidisciplinary Digital Publishing Institute
CitationSigcha, L.; Pavón, I.; Costa, N.; Costa, S.; Gago, M.; Arezes, P.; López, J.M.; De Arcas, G. Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 2021, 21, 291.
Abstract(s)Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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