arXiv:2606. 24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral responses.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
arXiv:2602. 01910v2 Announce Type: replace Abstract: Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes.