Medical signals augmentation for Parkinson’s disease diagnosis in low-resource settings across time, activity and patients

Automated Parkinson's diagnosis(PD) through wearable intelligence technologies has achieved considerable results, but its application in the wild environment presents challenges due to small on-state samples and sparse distribution across time, activity patterns, and subjects. To tackle these challenges, this study proposes a novel data augmentation model to improve PD recognition results in the wild. Our model utilizes a three-level augmentation strategy across different times, patterns, and subjects. Specifically, we apply temporal-level augmentation and aggregation to learn distinct representations, while using pattern/subject-level combinations and augmentations to generate additional samples. As a result, this augmentation approach not only facilitates the acquisition of diverse representations for symptoms but also addresses challenges such as missing data and small sample sizes, which are common for medical data in a free-living environments. This proposed model has applied to a real Parkinson's Disease (PD) dataset collected in low-resource settings, where it achieves impressive accuracy in the fine-grained classification of PD severity (mild, moderate, severe). In conclusion, this study contributes to more accurate PD self-diagnosis in real-world environments, thereby enabling remote drug intervention guidance from doctors.