  Health monitoring applications increasingly rely on machine learning
techniques to learn end-user physiological and behavioral patterns in everyday
settings. Considering the significant role of wearable devices in monitoring
human body parameters, on-device learning can be utilized to build personalized
models for behavioral and physiological patterns, and provide data privacy for
users at the same time. However, resource constraints on most of these wearable
devices prevent the ability to perform online learning on them. To address this
issue, it is required to rethink the machine learning models from the
algorithmic perspective to be suitable to run on wearable devices.
Hyperdimensional computing (HDC) offers a well-suited on-device learning
solution for resource-constrained devices and provides support for
privacy-preserving personalization. Our HDC-based method offers flexibility,
high efficiency, resilience, 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 the energy
efficiency of training by up to $45.8\times$ compared with the state-of-the-art
Deep Neural Network (DNN) algorithms while offering a comparable accuracy.
