ArticleNPJ Parkinson's disease2025
Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson's disease.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Smartphone-Based Multimodal Digital Biomarker Integration for Parkinson's Disease Screening and Diagnostic Support.Neurology and therapy · 2026Article
- Pilot Study Employing a Machine Learning Approach as a Potential Method for Predicting Parkinson's Disease Using Voice as a Digital Biomarker and the SHAP Approach for Feature Engineering.Bioengineering (Basel, Switzerland) · 2026Article
- In-clinic validation of a smartphone-based finger tapping test for use in neurodegenerative and neurological populations.medRxiv : the preprint server for health sciences · 2026Article
- Photonic and nanomaterial-driven optical biosensing strategies for Parkinson's disease.Mikrochimica acta · 2026Review
- What are you looking at? Modality contribution in multimodal medical deep learning.International journal of computer assisted radiology and surgery · 2026Article
- GRU-Based Deep Multimodal Fusion of Speech and Head-IMU Signals in Mixed Reality for Parkinson's Disease Detection.Sensors (Basel, Switzerland) · 2026Article
- Cross-sectional stratification of fall-related vulnerability in Parkinson's disease through multimodal fusion of speech, gait, facial expression, and upper-limb movement.Frontiers in digital health · 2026Article
- Dynamic context-aware multi-modal deep learning for longitudinal prediction of Parkinson's disease progression.Scientific reports · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.