arXiv:2608. 15951v1 Announce Type: new Abstract: We study when a wearable stress system should surface a prediction rather than change it.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 18279v1 Announce Type: cross Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal.
arXiv:2605. 22774v3 Announce Type: replace-cross Abstract: Assessing cognitive load continuously and at low latency would help adaptive human-computer interaction, but it remains hard because labeled data are scarce and models generalize poorly across subjects.
arXiv:2608. 08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks.
arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.
arXiv:2608. 16087v1 Announce Type: cross Abstract: Removing wearables from physiological monitoring also removes their supervision: the signal indicating where and when a stress response occurred.
arXiv:2607. 20237v1 Announce Type: new Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows.