Find out more about subscribing to add all events.
Current assessment of movement disorders relies on subjective rating scales and episodic clinic visits, while wearable solutions face compliance and ecological-validity limits. We present a contactless platform built around multiple synchronized millimeter-wave radars as the primary modality, complemented by video (IMU and depth-camera signals are used only during algorithm development), keeping the clinical workflow fully non-contact. Starting with Parkinson's disease for its standardized assessment and unmet clinical need, this talk covers our radar-centric system design, radar–video fusion, clinical validation, and roadmap toward broader movement disorders.
Haoxuan Li is a PhD candidate working at the interface of clinical neurology and sensing technology. He is developing a contactless, clinically grounded multimodal platform for the quantitative assessment of motor manifestations of neurological disease. His current focus spans QMG-aligned assessment of myasthenia gravis and MDS-UPDRS–aligned measurement of Parkinson's disease.