HB‑PVI is a hierarchical Bayesian framework that jointly models participant heterogeneity, the benefits and harms of four personalization mechanisms, and the economic value of acquiring additional labels for complex activity recognition. In a 47‑participant MUSIC‑CAR cohort, the framework used a sequential‑Monte‑Carlo updater, a Student‑t hierarchical gain model, and a one‑step expected‑value‑of‑sample‑information stopping rule. The results showed that, under realistic cost and benefit thresholds, the policy avoided labeling entirely, matching an always‑stop strategy while maintaining near‑optimal predictive performance and reducing labeling effort by 100%.
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...
arXiv:2608. 19501v1 Announce Type: cross Abstract: Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
BayaHAR is a lightweight, gradient‑free framework that adapts pretrained sensor‑based Human Activity Recognition (HAR) classifiers to new users by converting them into Prototypical Networks with prior prototypes that maintain zero‑shot performance. It introduces closed‑form Bayesian prototype estimation for labeled calibration data and extends this approach to weakly labeled data, requiring only activity labels. With just three seconds of calibration per activity, supervised adaptation boosts test macro‑F1 on unseen users by 2.76–33.44 percentage points across four datasets, while weakly supervised adaptation improves by 0.56–32.13 points, enabling efficient on‑device personalization.
How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-specialized experts, given a vector of trade-off wei...
arXiv:2609.23212v1 Announce Type: cross Abstract: The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) in...
Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed in the laboratory. A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user.
arXiv:2609.36154v1 Announce Type: new Abstract: General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity...
arXiv:2606. 04798v1 Announce Type: new Abstract: Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement.
arXiv:2609.28427v1 Announce Type: new Abstract: Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We e...
The paper introduces a framework that distinguishes two causes of saturation in clinical prediction: a learner gap, where the model fails to use available information, and a measurement‑channel ceiling, where the recorded variables limit performance. It provides theoretical characterizations, finite‑sample diagnostics, and empirical audits across three large cohorts, showing that well‑tuned models approach the frontier while deficient learners leave large gaps. A PRISMA‑guided synthesis across 104 tasks reveals consistent channel‑level patterns, suggesting that improving the learner or the measurement channel can audit and potentially lift performance.
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
arXiv:2606. 18203v1 Announce Type: cross Abstract: The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access.