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Health monitoring applications increasingly rely on machine learningtechniques to learn end-user physiological and behavioral patterns in everydaysettings. Considering the significant role of wearable devices in monitoringhuman body parameters, on-device learning can be utilized to build personalizedmodels for behavioral and physiological patterns, and provide data privacy forusers at the same time. However, resource constraints on most of these wearabledevices prevent the ability to perform online learning on them. To address thisissue, it is required to rethink the machine learning models from thealgorithmic perspective to be suitable to run on wearable devices.Hyperdimensional computing (HDC) offers a well-suited on-device learningsolution for resource-constrained devices and provides support forprivacy-preserving personalization. Our HDC-based method offers flexibility,high efficiency, resilience, and performance while enabling on-devicepersonalization and privacy protection. We evaluate the efficacy of ourapproach using three case studies and show that our system improves the energyefficiency of training by up to $45.8\times$ compared with the state-of-the-artDeep Neural Network (DNN) algorithms while offering a comparable accuracy.

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing