John Martins | Searcharxiv

arXiv2026

Towards Knitted Textile Electromechanical Systems

E-textiles and wearable sensing technologies enable flexible, customizable interfaces for human-computer interaction, with capacitive sensing offering precise touch and pressure detection. While machine knitting provides scalable, mechanically tunable structures ideal for such sensors, few studies develop or characterize insulated conductive yarns engineered for knitting's complex structural geometry and high flexure strain. In this work, we present a yarn dip-coating process, driven by an adjusted dip-coating fluid dynamics model, that enables scalable, machine knittable fabrication of capacitive tactile pressure sensing arrays. We establish optimal dip-coating parameters and concentrations of thermoplastic polyurethane (TPU) dissolved in dimethylformamide (DMF) to create knitting-optimized coatings (~630 um thickness). These fabricated yarns are shown to maintain electromechanical characteristics with minimal deviation after knitting and washing, thus allowing the creation of knitted pressure sensors through multi-layered structures. This process demonstrates that machine knitting with insulated yarns is a viable and reliable manufacturing approach to integrate sensing functionality into wearable textiles.

cs.HC↗

arXiv2025

Read the Room: Inferring Social Context Through Dyadic Interaction Recognition in Cyber-physical-social Infrastructure Systems

Cyber-physical systems (CPS) integrate sensing, computing, and control to improve infrastructure performance, focusing on economic goals like performance and safety. However, they often neglect potential human-centered (or ''social'') benefits. Cyber-physical-social infrastructure systems (CPSIS) aim to address this by aligning CPS with social objectives. This involves defining social benefits, understanding human interactions with each other and infrastructure, developing privacy-preserving measurement methods, modeling these interactions for prediction, linking them to social benefits, and actuating the physical environment to foster positive social outcomes. This paper delves into recognizing dyadic human interactions using real-world data, which is the backbone to measuring social behavior. This lays a foundation to address the need to enhance understanding of the deeper meanings and mutual responses inherent in human interactions. While RGB cameras are informative for interaction recognition, privacy concerns arise. Depth sensors offer a privacy-conscious alternative by analyzing skeletal movements. This study compares five skeleton-based interaction recognition algorithms on a dataset of 12 dyadic interactions. Unlike single-person datasets, these interactions, categorized into communication types like emblems and affect displays, offer insights into the cultural and emotional aspects of human interactions.

cs.CV↗