The paper introduces SpectrumAudit, a label‑sealed auditing method for wearable human‑activity recognition models that uses phase‑randomized full‑window stimuli to probe sensor biases. By replaying the DC component and a zero‑mean residual on held‑out subjects, the audit demonstrates significant accuracy drops across 27 victim models, with DC perturbations proving more harmful than AC in most cases. The study also shows that the audit can distinguish between persistent sensor offsets and zero‑mean variations under a fixed peak‑budget.
By Qingyu Wu, Yuan Wei, Renju Liu, Hua Cheng
arXiv:2608. 15951v1 Announce Type: new Abstract: We study when a wearable stress system should surface a prediction rather than change it.
By Jaden Moon, Yu Wu, Arvind Pillai, Andrew Campbell
arXiv:2607. 27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed.
By Jethro Odeyemi, W. J. Zhang
FemWear is a parameter‑efficient wearable foundation model specifically tailored for women's health. It repurposes a pretrained multimodal wearable backbone by training only 239,236 encoder parameters—just 1.11% of the original 21.54M—using low‑rank residual adapters and causal task‑family heads to create a shared longitudinal representation for menstrual, symptom, affective, sleep/recovery, autonomic, activity, and pregnancy outcomes. Evaluations across six cohorts and 63 metrics show improvements in cycle‑phase macro‑F1 and reductions in mean absolute error for cramps, mood symptoms, and sleep problems, while maintaining the OpenMHC ability‑retention benchmark.
By Yifan Wang, Chenzhong Li
arXiv:2609.14765v1 Announce Type: cross
Abstract: Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidime...
By Yun Chen, Oluwasegun T. Akinniyi, Qiang Zhang
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.
By Yifan Wang, Chenzhong Li
arXiv:2607.12176v2 Announce Type: replace
Abstract: Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the...
By Josep Cabacas-Maso, Ismael Benito-Altamirano, Carles Ventura
The paper presents a training‑free method for detecting which holds a climber uses in sport climbing videos by leveraging a frozen foundation pose model (Sapiens) that provides fingertip and toe keypoints. Using a simple proximity test, mutual exclusion, and a temporal‑persistence rule, the approach achieves high F_1 scores (up to 90.2%) on the Way Up dataset without any climbing‑specific training, outperforming repurposed pose pipelines. The resulting automatic predictions enable accurate coaching statistics, such as climb time and pace, with Pearson correlations of 1.00 and 0.94 respectively.
By Abu Bakar, Abdullah Aftab, Amir Hamza
The paper proposes Dynamic Influence-Weighted Distillation (DIW) to improve single-IMU activity recognition by leveraging a frozen four-IMU teacher during training. DIW assigns sample-wise gates to logit and feature losses, outperforming both supervised learning and fixed-weight knowledge distillation on the WEAR dataset with a macro‑F1 of 0.638451. The method enhances a right‑arm IMU model without altering the deployed sensor setup or the student network architecture.
By Bingxuan Xie
The paper introduces the Individual Conformal Coupling Monitor (ICCM), a lightweight pre‑inference tool that detects structural ambiguity—when physiological signals that appear plausible individually form a pattern poorly supported by a person’s non‑stress baseline—in wearable stress classifiers. On the WESAD dataset, a Random Forest achieves high mean accuracy but fails entirely for Subject 14 due to weakened cross‑signal coupling near stress onset. ICCM quantifies subject‑specific coupling divergence and can route data to classify, defer, or abstain, reducing false positives slightly and withholding some misclassified windows, though it does not fully correct the failure.
By Saba A. Farahani, Hung Cao, Amir M. Rahmani
arXiv:2608.22233v1 Announce Type: cross
Abstract: Test-time adaptation (TTA) aims to improve model robustness under distribution shift by adapting a source model using unlabeled test data. Although m...
By Sreeja Guha Majumdar, Aratrika Saha
arXiv:2606. 00141v1 Announce Type: cross Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limited data budgets, yet their benefits across individuals remain poorly understood.
By Ali Kargarandehkordi