Explores platform-agnostic signal compression techniques to enable efficient physiological data processing on resource-constrained wearable devices.
Addresses the critical need for privacy preservation in EEG data captured by commercial headwear, given its sensitive nature regarding mental states and health conditions.
An intra-oral piezoelectric sensing system integrated into a dental retainer for automated occlusal disease recognition using deep learning.
Fabricated PVDF-type sensors in dental retainers and developed ML solutions to detect occlusal disease, evaluated on 12 patients.