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Found 6 papers, 1 papers with code
Leveraging LLMs to Predict Affective States via Smartphone Sensor Features
Our findings reveal that LLMs can make promising predictions of affect measures using solely smartphone sensing data.
Enabling On-Device LLMs Personalization with Smartphone Sensing
To the best of our knowledge, this is the first framework to provide on-device LLMs personalization with smartphone sensing.
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.
