mmTremor: Practical Tremor Monitoring for Parkinson's Disease and Essential Tremor in Daily Life

Tremor, a prevalent symptom in various neurological disorders, significantly impacts patients' quality of life. Regular and precise tremor monitoring is essential for optimizing treatment effectiveness. Existing at-home solutions have limitations in practical daily life monitoring due to low adherence, privacy concerns, or inevitable interference from confounding body components and overlapped motions. In this paper, we propose mmTremor, the first privacy-preserving contactless system to achieve practical tremor detection during activities of daily living (ADL) in real-world settings. We design a mmWave-depth fusion tracking algorithm to handle the high interference from confounding body components. To combat the impact of activities, we design a multimodal spatiotemporal deep learning pipeline to effectively exploit latent tremor information. Additionally, a spatial contrastive unsupervised adaptation is proposed for better discriminability and adaptation capability to unseen users and environments. To fully assess mmTremor in real-world scenarios, we collect a diverse dataset of 28 patients and 9 healthy subject simulations in more than 20 distinct environments, including offices, hospitals, and homes. Extensive evaluations demonstrate that mmTremor achieves a high macro-F1 of 0.877 in tremor detection, showcasing the potential as a transformative contactless solution for tremor monitoring in daily life. The dataset will be open-source to facilitate future research.