A Dual-Task Gait Fusion Framework for Classifying Parkinson's Disease Severity from Wearable Sensor Data.
A Dual-Task Gait Fusion Framework for Classifying Parkinson's Disease Severity from Wearable Sensor Data.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Harbin, CN · Author affiliation
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.Location evidence
Zhangjiakou, CN · Author affiliation
School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075000, China.Location evidence
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Original abstract
Objectives: Accurate severity classification is important for sensor-based assessment of Parkinson's disease, but overlap between adjacent stages and class imbalance can reduce model robustness. This study aimed to develop and evaluate a dual-task gait fusion framework that integrates signals collected during self-selected walking and walking with turning. Methods: The primary cohort comprised 87 participants with Parkinson's disease across mild, mild-to-moderate, and moderate stages. Task-specific temporal representations were learned from the two gait conditions, concatenated and optimized using delayed class re-weighting. Performance was evaluated using subject-level stratified five-fold cross-validation with five random seeds. Generalizability was further assessed using an independent PhysioNet cohort of 93 participants with Parkinson's disease. Results: The proposed method achieved an accuracy of 90.23 ± 1.27, balanced accuracy of 89.61 ± 1.13, macro F1-score of 89.10 ± 1.35, and probability-based macro AUC of 94.43 ± 1.42 on WearGait-PD. Confusion matrix and ablation analyses indicated balanced class-level performance and complementary contributions from dual-task fusion and delayed re-weighting. On the external PhysioNet cohort, accuracy, balanced accuracy, macro F1-score, and macro AUC were 84.34 ± 1.82, 82.13 ± 1.92, 81.68 ± 1.98, and 88.93 ± 2.14, respectively. Conclusions: Integrating turning-related gait information with self-selected walking signals improved wearable sensor-based severity classification and showed cross-dataset robustness, although validation in larger cohorts with complete severity stage coverage remains necessary.