Detecting and monitoring the symptoms of Parkinson's disease using smartphones : a pilot study
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21359228
- Type
- D - Journal article
- DOI
-
10.1016/j.parkreldis.2015.02.026
- Title of journal
- Parkinsonism and Related Disorders
- Article number
- -
- First page
- 650
- Volume
- 21
- Issue
- 6
- ISSN
- 1353-8020
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
6
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 138
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Demonstrates the first use of smartphones, signal processing and machine learning for objectively quantifying the symptoms of Parkinson's remotely and non-invasively. Using built-in sensors (accelerometer, touch screen and microphone), users perform simple behavioural tasks such as walking, making 'aaah' sounds or tapping on-screen prompts. Machine learning maps the sensor data onto standard clinimetric scales. Early prototype for Apple's high-impact ResearchKit and mPower software. "Demonstrator" for similar app developed by Roche and now used for real-world clinical trials of novel drug treatments for Parkinson's. App now widely used in many influential academic studies worldwide. Evidence for mPower software popularity: https://www.nature.com/articles/sdata201611
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
