Abstract
Soft capacitive sensing in wearable textiles has many promising applications such as health monitoring, rehabilitation, or intelligent sports garments requiring sensitivity and sensory stability. Existing approaches either rely on rigid or semi-rigid components that compromise conformability, or on conductive yarn arrangements whose sensing geometry is fixed by the textile structure and difficult to adapt at design time. Crochet is an additive, stitch-by-stitch construction process which offers a largely unexplored route to programmable sensing surfaces, in which electrode geometry, baseline capacitance, and sensing range can be tuned directly through fabrication. We present a means of programmable capacitive sensing through crochet, enabling simultaneous integration of sensing functionality and aesthetic design within an additive, stitch-by-stitch manufacturing process. The system detects three distinct interaction modalities, namely proximity, soft contact, and pressure, with capacitance values mapped linearly to the brightness of integrated LED strings, producing expressive, real-time visual feedback through closed-loop control. Deformable and conformable to curved surfaces, the sensor maintains consistent behavior across positions and environments through automated adaptive baseline calibration. To explore applied pressure quantification, a Long Short-Term Memory (LSTM) neural network was trained to predict pressure from raw capacitance data, achieving an accuracy of 87.1% on the test data. The approach is demonstrated through Hooked, a crocheted wearable garment that functions as a distributed, fully soft capacitive sensing system with integrated light feedback, deformable and conformable to curved body surfaces. Four patterns originally drawn by hand illustrate both sensing and personal aesthetic expression. Hooked sits at the intersection of hand craft, computational design, and tactile expression: crochet functions here as an additive manufacturing process with a social, participative dimension, while capacitive sensing transforms the resulting textile into an expressive, touch-responsive interface.
Keywords
Tactile Sensing; Capacitive e-Textile; Crochet; Data-Driven Force Estimation
DOI
10.21606/TI-2026.130
Citation
Harsv, J.,and Hughes, J.(2026) Hooked: Programmable, Capacitive Sensing through Crochet as a Medium for Garments with Tactile Interaction, in Jane Scott, Sophie Skach, and Rebecca Stewart (eds.), Textile Intersections 2026, 17-18 September 2026, London, United Kingdom. https://doi.org/10.21606/TI-2026.130
Conference Track
researchpapers
Included in
Hooked: Programmable, Capacitive Sensing through Crochet as a Medium for Garments with Tactile Interaction
Soft capacitive sensing in wearable textiles has many promising applications such as health monitoring, rehabilitation, or intelligent sports garments requiring sensitivity and sensory stability. Existing approaches either rely on rigid or semi-rigid components that compromise conformability, or on conductive yarn arrangements whose sensing geometry is fixed by the textile structure and difficult to adapt at design time. Crochet is an additive, stitch-by-stitch construction process which offers a largely unexplored route to programmable sensing surfaces, in which electrode geometry, baseline capacitance, and sensing range can be tuned directly through fabrication. We present a means of programmable capacitive sensing through crochet, enabling simultaneous integration of sensing functionality and aesthetic design within an additive, stitch-by-stitch manufacturing process. The system detects three distinct interaction modalities, namely proximity, soft contact, and pressure, with capacitance values mapped linearly to the brightness of integrated LED strings, producing expressive, real-time visual feedback through closed-loop control. Deformable and conformable to curved surfaces, the sensor maintains consistent behavior across positions and environments through automated adaptive baseline calibration. To explore applied pressure quantification, a Long Short-Term Memory (LSTM) neural network was trained to predict pressure from raw capacitance data, achieving an accuracy of 87.1% on the test data. The approach is demonstrated through Hooked, a crocheted wearable garment that functions as a distributed, fully soft capacitive sensing system with integrated light feedback, deformable and conformable to curved body surfaces. Four patterns originally drawn by hand illustrate both sensing and personal aesthetic expression. Hooked sits at the intersection of hand craft, computational design, and tactile expression: crochet functions here as an additive manufacturing process with a social, participative dimension, while capacitive sensing transforms the resulting textile into an expressive, touch-responsive interface.