Assessment of Tremor Severity in Patients with Essential Tremor Using Smartwatches
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http://hdl.handle.net/2183/25743
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Assessment of Tremor Severity in Patients with Essential Tremor Using SmartwatchesAutor(es)
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2017Cita bibliográfica
Velasco, M.A., López Blanco, R., Romero Muñoz, J.P., Castillo Sobrino, M.D., Serrano Moreno, J.I., Benito León, J., Rocón de Lima, E . B Assessment of Tremor Severity in Patients with Essential Tremor Using Smartwatches. En Actas de las XXXVIII Jornadas de Automática, Gijón, 6-8 de Septiembre de 2017 (pp.347-352). DOI capítulo: https://doi.org/10.17979/spudc.9788497497749.0347 DOI libro: : https://doi.org/10.17979/spudc.9788497497749
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http://hdl.handle.net/10651/46514
Resumen
[Abstract] This paper presents a classification model for the automatic quantification of tremor severity in patients with essential tremor (ET). The system is based on the signals measured by two commercial smartwatches that the patients wear on their wrist and ankle. The smartwatches register acceleration and angular velocity in these body segments. A set of nine tremor features were used to train the classification algorithm. The proposed algorithm is based on a C4.5 decision tree classifier. It is able to assess rest and kinetic (postural or action) tremor. The method was evaluated using data collected from thirty-four patients with ET. The algorithm classifies the severity of tremor in five levels 0-4 corresponding to those in the Fahn-Tolosa-Marin tremor rating scale with a 94% accuracy. The method can be implemented in a networked platform for the remote monitoring and assessment of movement disorders such as ET or Parkinson’s disease.
Palabras clave
Tremor assessment
Time series classification
Essential tremor
Time series classification
Essential tremor
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Atribución-NoComercial-CompartirIgual 4.0 España
ISBN
978-84-16664-74-0 (UOV) 978-84-9749-774-9 (UDC electrónico)