Srijan Sen | Searcharxiv

arXiv2026

Sparse Longitudinal Functional Principal Component Analysis for Episodic Ambulatory Behavioral Assessments

Accurately monitoring mental fatigue is critical for improving workplace safety and productivity. A recent study examined unobtrusively collected smartphone typing speed as a potential ambulatory proxy assessment of mental fatigue using data from the Intern Health Study (IHS). While population-level average typing speed patterns were found to be consistent with validated measures of mental fatigue, how these trajectories vary across participants and days may inform opportune moments for just-in-time interventions and remains an open question. Treating typing speed trajectories as sparsely observed functional data, we propose a novel sparse longitudinal functional principal component analysis (sparse LFPCA) method for decomposing variability and predicting individual curves. Specifically, sparse data are accommodated by casting covariance estimation as a structured penalized spline regression problem, enabling simultaneous estimation and smoothing of multiple covariance components while borrowing information across locations in the functional domain. Simulations show that sparse LFPCA (1) accurately estimates eigenfunctions and generates reasonable predictions for underlying curves, and (2) achieves similar or superior performance compared to existing alternatives. Our analysis of typing speed data collected from IHS reveals new and interpretable participant- and day-level patterns not captured by previous analyses and can be used to tailor behavioral interventions.

stat.ME↗

arXiv2026

Ethical Fairness in Ubiquitous Health Sensing without Known Attributes

In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data. In these settings, high accuracy alone is insufficient; models must act ethically and equitably across diverse people, contexts, and devices. However, fairness methods that rely on demographic or heterogeneous attributes during training are difficult to enforce because such attributes are often unavailable, privacy-sensitive, regulated, or undesirable to collect. Conventional parity-based fairness can also violate ethical principles by trading off subgroup performance. To address this challenge, we present Flare, Fisher-guided LAtent-subgroup learning with do-no-harm REgularization, a demographic- and heterogeneous-attribute-agnostic framework that aligns human-centered fairness with ethical principles for ubiquitous and mobile sensing. Flare leverages optimization geometry, particularly Fisher Information, to regularize curvature and uncover latent disparities in model behavior without demographic or heterogeneous attributes. By integrating representation, loss, and curvature signals, it identifies hidden performance strata and refines them through collaborative but do-no-harm optimization, enhancing subgroup performance while preserving ethical balance. We also introduce BHE (Beneficence-Harm Avoidance-Equity), a metric suite that operationalizes ethical fairness beyond statistical parity. Across mobile physiological, behavioral, and clinical sensing datasets, including EDA, OhioT1DM, IHS, and Percept-R, Flare improves ethical fairness over state-of-the-art baselines. Ablation, interpretability, and loss-landscape analyses show that these gains arise from flatter optimization geometry, simpler decision rules, and do-no-harm latent-subgroup adaptation. Runtime analysis supports the practicality of Flare for resource-constrained sensing deployments.

cs.CY↗