Parkinson’s Disease Classification from Data Collected Using Smartphone: A Review of the Literature

Authors

  • Decho Surangsrirat National Science and Technology Development Agency, Pathum Thani, Thailand
  • Warisara Asawaponwiput Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand
  • Natsue Yoshimura Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
  • Apichart Intarapanich National Electronics and Computer Technology Center, Pathum Thani, Thailand
  • Denchai Worasawate Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand

Keywords:

Parkinson’s Disease, mPower study, Disease Classification

Abstract

Currently, diagnosis or severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependent on the skills and experiences of the trained specialist who performs the procedure. In order to quantify the disease and severity systematically, we investigate the studies on the feasibility of using a smartphone for the diagnosis of Parkinson’s disease (PD). The mPower dataset is one of the largest, open to researcher access, PD studies. It is a mobile application-based study for monitoring key indicators of PD progression. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS, PDQ-8, memory, tapping, voice, and  walking. The dataset has been analyzed and investigated by many research teams. Strong evidence supports that classifying or disease progression monitoring of PD from smartphone data is feasible with high accuracy, especially from voice and walking activities.

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Published

2022-04-28

How to Cite

[1]
Surangsrirat, D., Asawaponwiput, W., Yoshimura, N., Intarapanich, A. and Worasawate, D. 2022. Parkinson’s Disease Classification from Data Collected Using Smartphone: A Review of the Literature. Asian Medical Journal and Alternative Medicine. 22, 1 (Apr. 2022), 50–58.

Issue

Section

Review Articles