Research Article
A Review of Parkinson’s Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems
@INPROCEEDINGS{10.1007/978-3-319-51234-1_8, author={Somayeh Aghanavesi and Jerker Westin}, title={A Review of Parkinson’s Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems}, proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers}, proceedings_a={HEALTHYIOT}, year={2017}, month={1}, keywords={Parkinson’s Disease Sensors Objective assessment Motor symptoms Machine learning Dyskinesia Bradykinesia Rigidity Tremor}, doi={10.1007/978-3-319-51234-1_8} }- Somayeh Aghanavesi
Jerker Westin
Year: 2017
A Review of Parkinson’s Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-51234-1_8
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.