arXiv subjects

Sarmistha Sarna Gomasta

Publications and source records attributed to Sarmistha Sarna Gomasta.

5 recordsLinked to original sources

arXiv2026

NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems

Wearable EEG systems may expose sensitive information beyond their intended health function, creating substantial risks to neuroprivacy. In this work, we show that commonly used EEG features can reveal participant identity and demographic attributes in addition to supporting the intended cognitive task. Wearable EEG is increasingly being explored for cognitive monitoring, neurological assessment, and longitudinal digital-health applications, yet many systems assume that transmitting compact spectral or spatial features instead of raw EEG provides sufficient privacy protection. Using EEGMAT as a motivating case study, we find that compact EEG features achieve a balanced accuracy of 0.788 for cognitive-state classification while enabling gender, age, and subject-identity inference with balanced accuracies of 0.858, 0.789, and 0.692, respectively. We further show that privacy-aware representation learning preserves task performance at 0.781 while reducing these inference accuracies to 0.563, 0.467, and 0.206. These findings motivate purpose-limited representations and explicit privacy auditing in wearable neurohealth systems.

cs.CR↗

arXiv2026

ErgoAssist: Cognition-Aware Posture Feedback in Wearable Ergonomic Systems

Prolonged digital device use has made poor posture and musculoskeletal discomfort pervasive among knowl- edge workers. Existing ergonomic wearables rely solely on posture thresholds, frequently interrupting users during high-focus moments and leading to alert fatigue and abandonment. Yet posture and cognitive load are closely coupled, and most systems remain cognitively unaware. We present ErgoAssist, a head-worn ergonomic assistant that detects poor posture using IMU-based head tracking and estimates task-induced cognitive load using a consumer-grade EEG headband for continuous everyday use. In a controlled lab study, ErgoAssist achieves 81% posture classification and 90.2% task induced cognitive load estimation accuracy under leave-one-subject-out evaluation. In a preliminary real-time deployment, cognition-aware alerting reduces alert frequency by 81%, improves perceived usability by 43%, task performance by 25%, and improves posture correction rate by 38%, delivering fewer but better-timed interventions rather than merely suppressing alerts.

cs.HC↗

arXiv2026

Investigating How Music Affects Persuasion, Engagement, and Emotion in Data Videos

Data videos have become a prominent vessel for communicating data to broad audiences, and a common object of study in information visualization. Many of these videos include music, yet the impact of music on how people experience data videos remains largely unexplored. We conducted a preregistered study into the effect of music across three dimensions: persuasion, engagement, and emotion. We showed online participants an existing data video (1) without any music, (2) with its generic default music, and (3) with custom music designed by a professional composer. We found that the default music helped make the data video more persuasive. However, the effects of custom music were more mixed, and we did not find that music increased engagement. In addition, and contrary to our expectations, our participants reported more intense emotions without music. Our study contributes new insights into the intersection of music and data visualization and is a first step toward guiding designers in creating impactful data-driven narratives.

cs.HC↗

arXiv2025

FeatureSense: Protecting Speaker Attributes in Always-On Audio Sensing System

Audio is a rich sensing modality that is useful for a variety of human activity recognition tasks. However, the ubiquitous nature of smartphones and smart speakers with always-on microphones has led to numerous privacy concerns and a lack of trust in deploying these audio-based sensing systems. This paper addresses this critical challenge of preserving user privacy when using audio for sensing applications while maintaining utility. While prior work focuses primarily on protecting recoverable speech content, we show that sensitive speaker-specific attributes such as age and gender can still be inferred after masking speech and propose a comprehensive privacy evaluation framework to assess this speaker attribute leakage. We design and implement FeatureSense, an open-source library that provides a set of generalizable privacy-aware audio features that can be used for wide range of sensing applications. We present an adaptive task-specific feature selection algorithm that optimizes the privacy-utility-cost trade-off based on the application requirements. Through our extensive evaluation, we demonstrate the high utility of FeatureSense across a diverse set of sensing tasks. Our system outperforms existing privacy techniques by 60.6% in preserving user-specific privacy. This work provides a foundational framework for ensuring trust in audio sensing by enabling effective privacy-aware audio classification systems.

cs.SD↗

arXiv2024

Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profiles

This paper explores a novel technique for improving recall in cross-language information retrieval (CLIR) systems using iterative query refinement grounded in the user's lexical-semantic space. The proposed methodology combines multi-level translation, semantic embedding-based expansion, and user profile-centered augmentation to address the challenge of matching variance between user queries and relevant documents. Through an initial BM25 retrieval, translation into intermediate languages, embedding lookup of similar terms, and iterative re-ranking, the technique aims to expand the scope of potentially relevant results personalized to the individual user. Comparative experiments on news and Twitter datasets demonstrate superior performance over baseline BM25 ranking for the proposed approach across ROUGE metrics. The translation methodology also showed maintained semantic accuracy through the multi-step process. This personalized CLIR framework paves the path for improved context-aware retrieval attentive to the nuances of user language.

cs.IR↗