Flexible and Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Year: 2022
Type: conference-paper
Abstract: Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We exploit a Hyperdimensional computing (HDC) solution for wearable devices that offers flexibility, high efficiency, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves performance of training by up to 35.8x compared with the state-of-the-art while offering a comparable accuracy.
Institution: University of California, Irvine
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4282020260