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A Review of Parkinson’s Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems

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Internet of Things Technologies for HealthCare (HealthyIoT 2016)

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Abstract

This paper is reviewing objective assessments of Parkinson’s disease (PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

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Authors and Affiliations

  1. Computer Science Department, Dalarna University, Rodavagen 3, S78188, Borlänge, Sweden

    Somayeh Aghanavesi & Jerker Westin

Authors
  1. Somayeh Aghanavesi
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  2. Jerker Westin
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Corresponding author

Correspondence to Somayeh Aghanavesi .

Editor information

Editors and Affiliations

  1. School Innovation, Design, Engineering, Märlardalen University, Västerås, Sweden

    Mobyen Uddin Ahmed

  2. Mälardalen University , Västerås, Sweden

    Shahina Begum

  3. Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Wasim Raad

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Aghanavesi, S., Westin, J. (2016). A Review of Parkinson’s Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-51234-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51233-4

  • Online ISBN: 978-3-319-51234-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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