In this section
Our research centres around the body and how technology can be used to improve how that body exists and interacts with the surrounding environment. We focus on haptic and aural modalities, using textiles as the physical medium for building wearable computational systems. Some of the research projects we undertake focus exclusively on textile sensing and interfaces whilst other focus solely on how auditory displays can be improved for users. A growing area of our work is looking towards how these two complementary technologies can be brought together in novel applications.
Below is a selection of projects grouped by theme of work:
Utilising novel textiles or electronic integrations to track and measure different forms of motion directly through fabric interventions.
Investigating next-generation haptic outputs embedded within textiles, with the unique ability to provide localised bodily sensations and tactile effects currently unavailable from other technologies.
Utilising novel textiles or electronic integrations to track and measure different forms of motion directly through fabric interventions.
Utilising novel textiles or electronic integrations to track and measure different forms of motion directly through fabric interventions.
The most developed strand of research in the group is tracking human motion through textile sensors. SensiKnit was developed by Dr Yi (Joy) Zhou during her PhD. SensiKnit is a graphene-based wearable monitoring system. The ergonomic sensors, crafted with digital knitting and laser-cutting, ensure close skin contact for accurate data collection and allow a full range of motion for user comfort. Integrated into wearables, SensiKnit can monitor body movements, such as knee bends and arm gestures, making it ideal for exercise interfaces and injury rehabilitation. Resistant to UV rays and washing, it offers consistent, real-time activity feedback under any condition.
This work has been published in Advanced intelligent Systems (Zhou, Y., Sun, Y., Li, Y., Shen, C., Lou, Z., Min, X. and Stewart, R. (2024), A Highly Durable and UV-Resistant Graphene-Based Knitted Textile Sensing Sleeve for Human Joint Angle Monitoring and Gesture Differentiation. Adv. Intell. Syst. 2400124. https://doi.org/10.1002/aisy.202400124).
The video was filmed and produced by Xiannuo Phoenix Zhao (Xcellent Productions Ltd).
@inproceedings{Wang:2026:10.1145/3776734.3794525,
author = {Wang, M and Li, Y and Nissen, B and Stewart, R},
doi = {10.1145/3776734.3794525},
pages = {902--906},
title = {MenstaRay: A Knitted Soft Wearable Robotic Interface for Somatosensory Communication of Menstrual Experience},
url = {http://dx.doi.org/10.1145/3776734.3794525},
year = {2026}
}
TY - CPAPER
AB - MenstaRay is a soft knit robotic interface designed to explore how tactile actuation can support somatosensory communication of menstrual experiences. The prototype was created using a fabrication method for knit-integrated soft wearable robotics with two core structural elements: (1) an extensible EcoFlex 00-10 silicone cavity containing internal air chambers and (2) a strain-limiting textile layer knitted with Spandex Super Stretch Yarn (81% nylon, 19% elastane). This configuration enables regulated inflation patterns that preserve the softness of textiles while providing targeted haptic feedback that is suitable for intimate, safe, and therapeutically appropriate interactions. Through a series of workshops, we investigated and evaluated how these dynamic tactile behaviours shaped participants’ embodied reflections on menstrual sensations. This work contributes to human robotic interaction by introducing MenstaRay, a novel artifact coupled with textile-integrated actuation that can externalize intimate bodily sensations and foster new modes of communicating, reflecting on and representing menstrual experiences through wearable interfaces.
AU - Wang,M
AU - Li,Y
AU - Nissen,B
AU - Stewart,R
DO - 10.1145/3776734.3794525
EP - 906
PY - 2026///
SP - 902
TI - MenstaRay: A Knitted Soft Wearable Robotic Interface for Somatosensory Communication of Menstrual Experience
UR - http://dx.doi.org/10.1145/3776734.3794525
ER -