Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease
Abstract Smart devices can easily capture changes in voice, movements, and gait in people with Parkinson’s disease (PD). We investigated whether smartphone-derived multimodal features combined with machine learning algorithms can aid in early PD identification. We recruited 496 participants, split i...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-025-00953-w |
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| _version_ | 1849311632539254784 |
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| author | Wee-Shin Lim Sung-Pin Fan Shu-I Chiu Meng-Ciao Wu Pu-He Wang Kun-Pei Lin Yung-Ming Chen Pei-Ling Peng Jyh-Shing Roger Jang Chin-Hsien Lin |
| author_facet | Wee-Shin Lim Sung-Pin Fan Shu-I Chiu Meng-Ciao Wu Pu-He Wang Kun-Pei Lin Yung-Ming Chen Pei-Ling Peng Jyh-Shing Roger Jang Chin-Hsien Lin |
| author_sort | Wee-Shin Lim |
| collection | DOAJ |
| description | Abstract Smart devices can easily capture changes in voice, movements, and gait in people with Parkinson’s disease (PD). We investigated whether smartphone-derived multimodal features combined with machine learning algorithms can aid in early PD identification. We recruited 496 participants, split into a training cohort (127 PD patients during “on” phase and 198 age-matched controls) and a test dataset (86 patients during “off” phase and 85 age-matched controls). Multidomain features from smartphone recordings were analyzed using machine learning classifiers with integration of a hyperparameter grid. Single-modality models for voice, hand movements, and gait showed diagnostic values of 0.88, 0.74, and 0.81, respectively, with test dataset values of 0.80, 0.74, and 0.76. An integrated multimodal model using a support vector machine improved performance to 0.86 and achieved 0.82 for identifying early-stage PD during the “off” phase. A smartphone-based integrated multimodality model combining voice, hand movement, and gait shows promise for early PD identification. |
| format | Article |
| id | doaj-art-8be60ed1813e44dc8a27ed252eb10cc8 |
| institution | Kabale University |
| issn | 2373-8057 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Parkinson's Disease |
| spelling | doaj-art-8be60ed1813e44dc8a27ed252eb10cc82025-08-20T03:53:21ZengNature Portfolionpj Parkinson's Disease2373-80572025-05-0111111410.1038/s41531-025-00953-wSmartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s diseaseWee-Shin Lim0Sung-Pin Fan1Shu-I Chiu2Meng-Ciao Wu3Pu-He Wang4Kun-Pei Lin5Yung-Ming Chen6Pei-Ling Peng7Jyh-Shing Roger Jang8Chin-Hsien Lin9Department of Computer Science and Information Engineering, National Taiwan UniversityDepartment of Neurology, National Taiwan University HospitalDepartment of Computer Science, National Chengchi UniversityDepartment of Electronic Engineering, National Taiwan UniversityDepartment of Computer Science and Information Engineering, National Taiwan UniversityDepartment of Geriatrics, National Taiwan University Hospital, College of Medicine, National Taiwan UniversityDepartment of Internal Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan UniversityDepartment of Neurology, National Taiwan University HospitalDepartment of Computer Science and Information Engineering, National Taiwan UniversityDepartment of Neurology, National Taiwan University HospitalAbstract Smart devices can easily capture changes in voice, movements, and gait in people with Parkinson’s disease (PD). We investigated whether smartphone-derived multimodal features combined with machine learning algorithms can aid in early PD identification. We recruited 496 participants, split into a training cohort (127 PD patients during “on” phase and 198 age-matched controls) and a test dataset (86 patients during “off” phase and 85 age-matched controls). Multidomain features from smartphone recordings were analyzed using machine learning classifiers with integration of a hyperparameter grid. Single-modality models for voice, hand movements, and gait showed diagnostic values of 0.88, 0.74, and 0.81, respectively, with test dataset values of 0.80, 0.74, and 0.76. An integrated multimodal model using a support vector machine improved performance to 0.86 and achieved 0.82 for identifying early-stage PD during the “off” phase. A smartphone-based integrated multimodality model combining voice, hand movement, and gait shows promise for early PD identification.https://doi.org/10.1038/s41531-025-00953-w |
| spellingShingle | Wee-Shin Lim Sung-Pin Fan Shu-I Chiu Meng-Ciao Wu Pu-He Wang Kun-Pei Lin Yung-Ming Chen Pei-Ling Peng Jyh-Shing Roger Jang Chin-Hsien Lin Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease npj Parkinson's Disease |
| title | Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease |
| title_full | Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease |
| title_fullStr | Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease |
| title_full_unstemmed | Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease |
| title_short | Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease |
| title_sort | smartphone derived multidomain features including voice finger tapping movement and gait aid early identification of parkinson s disease |
| url | https://doi.org/10.1038/s41531-025-00953-w |
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