arXiv subjects

Shubham Rohal

Publications and source records attributed to Shubham Rohal.

4 recordsLinked to original sources

arXiv2026

WAVES: Wearable Active Vibroacoustic Elasticity Sensing for Low-Cost and Non-Invasive Muscle Stiffness Monitoring

Muscle stiffness is a physiologically meaningful biomarker associated with a wide range of musculoskeletal and neurological conditions. However, existing muscle stiffness assessment methods, such as shear wave elastography (SWE), manual palpation, and handheld mechanical probes, remain impractical for continuous, everyday monitoring due to bulky hardware, operator dependence, and limited self-use. Meanwhile, wearable muscle-sensing modalities such as Electromyography (EMG) and Mechanomyography (MMG) primarily capture muscle activation rather than passive mechanical properties of tissue at rest, leaving a critical gap in portable monitoring of resting muscle stiffness. In this work, we present WAVES, a wearable system for continuous monitoring of muscle stiffness via active vibroacoustic sensing. WAVES injects controlled vibroacoustic excitation into soft tissue and estimates muscle stiffness by measuring shear-wave propagation velocity through Time-Difference-of-Arrival (TDoA) analysis across spatially distributed wearable sensors. To achieve accurate TDoA estimation with low-cost wearable hardware, we develop a physiology- and quality-guided vibroacoustic sensing framework that effectively screens and refines TDoA for accurate stiffness estimation. We evaluate WAVES through characterization experiments using ballistic jelly and end-to-end on-body evaluation with human participants. Results demonstrate that WAVES can robustly estimate muscle stiffness across multiple users and different superficial muscles while enabling lightweight, wearable deployment for everyday use. WAVES is able to achieve a muscle stiffness estimation error of 13.1% or less across multiple users and multiple muscle groups.

cs.ET↗

arXiv2026

OriFeel: Origami-Inspired Tactile Feedback via Surface Folding

People passively interact with ambient surfaces such as tables, chair backs, and armrests throughout daily life, making them natural candidates for ubiquitous tactile interfaces. However, transforming these everyday surfaces into practical haptic interfaces remains challenging. Existing solutions typically rely on dense arrays of actuators, resulting in bulky hardware and high power consumption that limit their integration into ambient objects. We present OriFeel, a structure-driven tactile interface that leverages a Miura-ori folding mechanism to distribute actuation across multiple interconnected folding units. Rather than mapping actuators to individual contact points, OriFeel uses embedded cables to exploit structural interconnections, distributing actuation across the surface and enabling spatially controllable tactile output across multiple folding units. We implement prototypes using rigid and soft materials and characterize their ability to distribute actuation across interconnected units. Our results demonstrate the feasibility of structure-driven tactile feedback via coordinated folding of compliant origami surface structures, providing a practical foundation for compact, scalable ambient haptic interfaces.

cs.HC↗

arXiv2026

GEM: Gear-based Environment-Integrated Mobility for Adaptive Indoor Human Sensing

Infrastructure-based sensing systems, like Wi-Fi, thermal, vibration-based approaches, provide continuous and unobtrusive indoor human monitoring services. They are often deployed statically for long-term continuous monitoring, which often leads to inefficient sensing/inflexible deployment due to human mobility or high maintenance/data volume for dense deployments. In contrast, autonomous and human carried mobile devices can better adapt to human mobility. However, their physical presence (e.g., drones or robots) may induce observer effects, while their operation often imposes additional burdens, such as wearing (e.g., wearables) and frequent charging. We present GEM, a hybrid scheme that introduces the mobility to infrastructure-based sensing. GEM integrates a matrix of gears into everyday surfaces (e.g., floors, walls) to turn them into "public transportation" for moving infrastructure sensors around. We design and fabricate a 3 x 3 gear matrix prototype that can effectively move sensors from one location to another. We further validate the scalability of the design through simulation of up to 64 x 64 gear matrix with concurrent sensors.

eess.SY↗

arXiv2025

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.

cs.LG↗