Assessment of Tremor Severity in Patients with Essential Tremor Using Smartwatches
- Miguel A. Velasco
- Roberto López-Blanco
- Juan P. Romero
- M. Dolores Del Castillo
- J. Ignacio Serrano
- Julián Benito-León
- Eduardo Rocon
- Hilario López García (coord.)
Editorial: Servicio de Publicaciones ; Universidad de Oviedo
ISBN: 978-84-16664-74-0
Año de publicación: 2017
Páginas: 347-352
Congreso: Jornadas de Automática (38. 2017. Gijón)
Tipo: Aportación congreso
Resumen
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.