RiM: Record, Improve and Maintain Physical Well-being using Federated Learning

In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health challenges. In this study, we introduce RiM: Record, Improve, and Maintain, a mobile application which incorporates a novel personalized machine learning framework that leverages federated learning to enhance students' physical well-being by analyzing their lifestyle habits. Our approach involves pre-training a multilayer perceptron (MLP) model on a large-scale simulated dataset to generate personalized recommendations. Subsequently, we employ federated learning to fine-tune the model using data from IISER Bhopal students, thereby ensuring its applicability in real-world scenarios. The federated learning approach guarantees differential privacy by exclusively sharing model weights rather than raw data. Experimental results show that the FedAvg-based RiM model achieves an average accuracy of 60.71% and a mean absolute error of 0.91--outperforming the FedPer variant (average accuracy 46.34%, MAE 1.19)--thereby demonstrating its efficacy in predicting lifestyle deficits under privacy-preserving constraints.

Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types

Electrocardiograms (ECGs) are essential for monitoring cardiac health, allowing clinicians to analyze heart rate variability (HRV), detect abnormal rhythms, and diagnose cardiovascular diseases. However, ECG signals, especially those from wearable devices, are often affected by noise artifacts caused by motion, muscle activity, or device-related interference. These artifacts distort R-peaks and the characteristic QRS complex, making HRV analysis unreliable and increasing the risk of misdiagnosis. Despite this, the few existing studies on ECG noise detection have primarily focused on a single dataset, limiting the understanding of how well noise detection models generalize across different datasets. In this paper, we investigate the generalizability of noise detection in ECG using a novel HRV-based approach through cross-dataset experiments on four datasets. Our results show that machine learning achieves an average accuracy of over 90\% and an AUPRC of more than 0.9. These findings suggest that regardless of the ECG data source or the type of noise, the proposed method maintains high accuracy even on unseen datasets, demonstrating the feasibility of generalizability.

Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals across Social Contexts

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety disorder. In this study, we present results using passively collected data from a within-subject experiment that assessed physiological response across different social contexts (i.e, alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study ($N=46$) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale ($\geq$34 out of 80). Univariate paired difference tests, multivariate random forest models, and follow-up cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, but that there is considerable variability in physiological response patterns even among these distinguishable contexts. Implications for real-world context detection and deployment of JITAIs are discussed.