Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.
The 7 matches
- [1] § Methods › CNN model training ↔ LOSO_Bootstrap CI_Sensitivity Analysis/Main_v4_Train_TSImgs_v3.py, lines 1425–1474 · score 0.80 · SqueezeNet, DenseNet, ResNet, TS images, CNN models, training
- [2] § Methods › Statistical analysis ↔ Machine Learning_Lasso/LASSO_Logistic_BinaryClassification.py, lines 395–455 · score 0.72 · LASSO logistic, LASSO selected feature, F1 score, zero, coefficients, ROC
- [3] § Methods › CNN model training ↔ Machine Learning_Lasso/LASSO_Logistic_BinaryClassification.py, lines 203–224 · score 0.69 · confusion matrix, binary classification, F1 score, recall, precision, metrics
- [4] § Results › CNN classification performance across gait phases ↔ LOSO_Bootstrap CI_Sensitivity Analysis/Main_v4_Train_TSImgs_v3.py, lines 1425–1474 · score 0.69 · SqueezeNet, DenseNet, ResNet, TS images, CNN, model
- [5] § Methods › CNN model training ↔ LOSO_Bootstrap CI_Sensitivity Analysis/Main_v4_Train_TSImgs_v3.py, lines 713–768 · score 0.67 · confusion matrix, binary classification, recall, sensitivity, precision, metrics
- [6] § Methods › Data generation ↔ LOSO_Bootstrap CI_Sensitivity Analysis/Main_v4_Train_TSImgs_v3.py, lines 436–536 · score 0.63 · minority class, randomly oversampled, training fold, segment, PD
- [7] § Methods › Statistical analysis ↔ LOSO_Bootstrap CI_Sensitivity Analysis/Main_v4_Train_TSImgs_v3.py, lines 436–536 · score 0.54 · minority class, random oversampling, sensitivity, training, fold
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
Python · 1,722 lines · 62 KB · no license · 5 matches
Main_v4_Train_TSImgs_v3.py at commit 123c6a5, no license · at the source
Overview
- Biomechanics Laboratory, Dong-A University,37 Nakdong-Daero 550 Beon-gil, Saha-gu, Busan, 49315 Republic of Korea
- DAU G-LAMP Project Group, Innovation Center for Atomic Science Dong-A University,37 Nakdong-Daero 550 Beon-gil, Saha-gu, Busan, 49315 Republic of Korea
- Department of Health Sciences, The Graduate School of Dong-A University,37 Nakdong-Daero 550 Beon-gil, Saha-gu, Busan, 49315 Republic of Korea
- Department of Neurology, School of Medicine, Dong-A University,32 Daesingongwon-ro, Seo-gu, Busan, 49201 Republic of Korea
Abstract
The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.
Repositories
Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.
Zenodo 20839352
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
5 files
- Deep Learning_CNN/
Main_v4_Gen_TSImg.py — Python, 67 lines - Deep Learning_CNN/
Main_v4_Train_TSImgs.py — Python, 155 lines - LOSO_Bootstrap CI_Sensitivity Analysis/
Main_v4_Train_TSImgs_v3. — Python, 1,722 linespy - Machine Learning_Lasso/
LASSO_Logistic_BinaryCla — Python, 535 linesssification.py - README.md — Text, 9 lines
hyejin-choi1/early-pd-wearable-sensor-cnn
123c6a541999a115cf5c2c275df38dbca3f1f75f, 25 June 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
5 files, not copied: shown from their source
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- Deep Learning_CNN/
Main_v4_Gen_TSImg.py — Python, 67 lines, shown from its source - Deep Learning_CNN/
Main_v4_Train_TSImgs.py — Python, 155 lines, shown from its source - LOSO_Bootstrap CI_Sensitivity Analysis/
Main_v4_Train_TSImgs_v3. — Python, 1,722 lines, 5 matches, shown from its sourcepy - Machine Learning_Lasso/
LASSO_Logistic_BinaryCla — Python, 535 lines, 2 matches, shown from its sourcessification.py - README.md — Text, 9 lines, shown from its source
Code availability statement
The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:
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Read it in the paper: doi.org/10.1038/s41598-026-61801-2.
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Read it in the paper: doi.org/10.1038/s41598-026-61801-2.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 12 keywords, 13 MeSH terms, 3 funders, 66 references.
Cite
This paper
Choi, H., Youm, C., Park, H., Kim, B., Hwang, J., & Cheon, S.-M. (2026). Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks. Scientific reports, 16(1), 26521. https://
BibTeX
@article{choi2026detecti
author = {Choi, Hyejin and Youm, Changhong and Park, Hwayoung and Kim, Bohyun and Hwang, Juseon and Cheon, Sang-Myung},
title = {{Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks}},
journal = {Scientific reports},
year = {2026},
month = jul,
volume = {16},
number = {1},
pages = {26521},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {42432239},
pmcid = {PMC13503714}
}
RIS
TY - JOUR
AU - Choi, Hyejin
AU - Youm, Changhong
AU - Park, Hwayoung
AU - Kim, Bohyun
AU - Hwang, Juseon
AU - Cheon, Sang-Myung
TI - Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 26521
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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