In-The-Wild Detection of Intermittent Parkinsonian Tremor: A Federated, Self-Supervised Learning Approach Using Attention-Based Multiple Instance Learning.

Parkinson's disease (PD) is a long-term neurode-generative disorder characterized by motor and non-motor symptoms, with tremor being a key indicator. Early detection of PD is vital for effective symptom management, yet current methods often struggle in real-world scenarios. This study addresses Parkinsonian tremor detection using smartphone-captured accelerometer data collected in-the-wild. We propose a novel approach that integrates contrastive pretraining within a pre-existing Multiple-Instance Learning (MIL) framework to leverage large-scale unlabeled data and enhance representation learning. Additionally, we extend the method to a federated learning framework for scalable and privacy-preserving deployment. Results confirm that the pretrained MIL model surpasses the baseline, with improved performance as the unlabeled dataset size increases. Furthermore, federated pretraining achieves comparable results to centralized training while maintaining privacy and addressing non-Independent and Identically Distributed (non-IID) data challenges.