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).
@article{Zhou:2024:10.1002/aisy.202400124,
author = {Zhou, Y and Sun, Y and Li, Y and Shen, C and Lou, Z and Min, X and Stewart, R},
doi = {10.1002/aisy.202400124},
journal = {Advanced Intelligent Systems},
title = {A highly durable and UVresistant graphenebased knitted textile sensing sleeve for human joint angle monitoring and gesture differentiation},
url = {http://dx.doi.org/10.1002/aisy.202400124},
volume = {6},
year = {2024}
}
TY - JOUR
AB - Flexible strain sensors based on textiles have attracted extensive attention owing to their light weight, flexibility, and comfort when wearing. However, challenges in integrating textile strain sensors into wearable sensing devices include the need for outstanding sensing performance, long-term monitoring stability, and fast, convenient integration processes to achieve comprehensive monitoring. The scalable fabrication technique presented here addresses these challenges by incorporating customizable graphene-based sensing networks into knitted structures, thus creating sensing sleeves for precise motion detection and differentiation. The performance and real-world application potential of the sensing sleeve are evaluated by its precision in angle estimation and complex joint motion recognition during intra- and intersubject studies. For intra-subject analysis, the sensing sleeve only exhibits a 2.34° angle error in five different knee activities among 20 participants, and the sensing sleeves show up to 94.1% and 96.1% accuracy in the gesture classification of knee and elbow, respectively. For inter-subject analysis, the sensing sleeve demonstrates a 4.21° angle error, and it shows up to 79.9% and 85.5% accuracy in the gesture classification of knee and elbow, respectively. An activity-guided user interface compatible with the sensing sleeves for human motion monitoring in home healthcare applications is presented to illustrate the potential applications.
AU - Zhou,Y
AU - Sun,Y
AU - Li,Y
AU - Shen,C
AU - Lou,Z
AU - Min,X
AU - Stewart,R
DO - 10.1002/aisy.202400124
PY - 2024///
SN - 2640-4567
TI - A highly durable and UVresistant graphenebased knitted textile sensing sleeve for human joint angle monitoring and gesture differentiation
T2 - Advanced Intelligent Systems
UR - http://dx.doi.org/10.1002/aisy.202400124
VL - 6
ER -