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Found 4 papers, 1 papers with code
UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers
This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection.
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint (by 178. 7x), end-to-end latency by 80. 8-94. 2%, and energy consumption by 80. 9-94. 2%.
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-consuming.
Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction
Deep learning models have shown great promise in various healthcare applications.
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