Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study
- DOI
- 10.1371/journal.pone.0236258
- Published
- 2020-07-23
- Container
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0236258,
title = {Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study},
author = {Milla Juutinen and Cassia Wang and Justin Zhu and Juan Haladjian and Jari Ruokolainen and Juha Puustinen and Antti Vehkaoja},
year = {2020},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0236258},
url = {https://doi.org/10.1371/journal.pone.0236258}
}RIS
TY - JOUR TI - Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study AU - Milla Juutinen AU - Cassia Wang AU - Justin Zhu AU - Juan Haladjian AU - Jari Ruokolainen AU - Juha Puustinen AU - Antti Vehkaoja PY - 2020 JO - PLOS ONE DO - 10.1371/journal.pone.0236258 UR - https://doi.org/10.1371/journal.pone.0236258 ER -
APA
Juutinen, M., Wang, C., Zhu, J., Haladjian, J., Ruokolainen, J., Puustinen, J., & Vehkaoja, A. (2020). Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLOS ONE. https://doi.org/10.1371/journal.pone.0236258
Source records
- crossref · retrieved 2026-09-27T06:53:50.967Z