Tremors quantification in Parkinson patients using smartwatches

R. Contreras, M. Huerta, G. Sagbay, C. Llumiguano, M. Bravo, A. Bermeo, R. Clotet, A. Soto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations


Parkinson's disease is a neurological disorder that affects about 1% of people over 60 years. The complexity of the disease difficult to carry out objective medical assessments of the level of tremors. This article presents a system which uses the accelerometer and gyroscope sensors in smartwatches to quantify the tremors in patients with Parkinson's disease. The system is based on a Wireless Body Area Network composed by multiple sensor nodes (Android Wears) and one sink node (Android Smartphone). The system integrates four processes: User authentication, placement of sensors on the body, movement sensing and data uploading. The system was evaluated in 12 patients with PD (five males and seven females) while they were doing several activities, but in this article we only analyses when the patients were seated at rest. The patients had an average disease duration of 6.25 years, an average age of 66.33 years and a range of 5186 years. The tremor magnitudes were presented in the form of linear acceleration and angular velocity in the time domain. The results indicate that these variables can determine Parkinson's disease evolution in a patient diagnosed with stage 3 and 4.

Translated title of the contributionCuantificación de temblores en enfermos de Parkinson con smartwatches
Original languageEnglish
Title of host publication2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509016297
StatePublished - 21 Nov 2016
Event2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 - Quito, Ecuador
Duration: 12 Oct 201614 Oct 2016

Publication series

Name2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016


Conference2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
Abbreviated titleETCM 2016

Bibliographical note

Funding Information:
The authors gratefully acknowledge the support of the MORENO project, Universidad Politécnica Salesiana from Cuenca-Ecuador, the Prometeo Project, SENESCYT from Ecuador, the PEII2012-783 Project, MPPCTI from Venezuela.

Publisher Copyright:
© 2016 IEEE.


  • Parkinson
  • smartwatches
  • tremors


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