A deep learning-based silhouette gait detection framework for fall risk classification in Parkinson's disease.

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BackgroundParkinson's disease (PD) is a rapidly growing neurodegenerative disorder associated with gait impairment, functional decline, and increased fall risk. Conventional clinical assessments rely heavily on brief observational scales and are limited by subjectivity and environmental bias. There is a pressing need for objective, quantitative, and scalable digital assessment tools to support precision management of PD.ObjectiveThis study aimed to develop and validate a smartphone-video-based gait assessment system aligned with a clinically meaningful pathway encompassing PD screening, functional evaluation, and fall risk classification.MethodsIn this cross-sectional observational study, patients with mild-to-moderate PD and age-matched healthy controls were recruited following Institutional Review Board approval. Gait videos were recorded using smartphone cameras and processed through a silhouette extraction pipeline. Spatiotemporal gait features were subsequently extracted using a MoviNet-based deep learning framework. A multi-task framework was developed to perform PD screening, activities of daily living functional grading based on the Schwab and England Activities of Daily Living (S&E ADL) scale, and fall risk classification. Model performance and generalizability were evaluated using subject-independent data partitioning and independent cross-site validation.ResultsA total of 40 participants were recruited. For PD screening, the model achieved an accuracy of 95.7%, a sensitivity of 96.6%, a specificity of 92.2%, and an AUC of 0.994 on the independent test set. For S&E ADL functional grading, the model achieved an overall accuracy of 88.5% across three functional categories (80, 90, and 100%). For fall risk classification, the model achieved an accuracy of 91.1%, a sensitivity of 68.4%, a specificity of 98.3%, and an AUC of 0.909. Overall, the framework demonstrated robust performance across PD screening, functional stratification, and fall risk classification tasks.ConclusionThis study developed a smartphone-video-based gait assessment framework using computer vision and deep learning. The proposed framework demonstrated strong performance in PD identification, severity estimation, and fall risk classification. Future work will expand validation across diverse populations and disease stages, while advancing toward multimodal integration and long-term home monitoring.

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