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Found 7 papers, 6 papers with code
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.
Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality.
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition.
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring.
Self-supervised transfer learning of physiological representations from free-living wearable data
Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level.
Learning Generalizable Physiological Representations from Large-scale Wearable Data
To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration.
