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{Lou:2024:10.1109/JBHI.2024.3417616,
author = {Lou, Z and Min, X and Li, G and Avery, J and Stewart, R},
doi = {10.1109/JBHI.2024.3417616},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {5855--5864},
title = {Advancing sensing resolution of impedance hand gesture recognition devices},
url = {http://dx.doi.org/10.1109/JBHI.2024.3417616},
volume = {28},
year = {2024}
}
TY - JOUR
AB - Gestures are composed of motion information (e.g. movements of fingers) and force information (e.g. the force exerted on fingers when interacting with other objects). Current hand gesture recognition solutions such as cameras and strain sensors primarily focus on correlating hand gestures with motion information and force information is seldom addressed. Here we propose a bio-impedance wearable that can recognize hand gestures utilizing both motion information and force information. Compared with previous impedance-based gesture recognition devices that can only recognize a few multi-degrees-of-freedom gestures, the proposed device can recognize 6 single-degree-of-freedom gestures and 20 multiple-degrees-of-freedom gestures, including 8 gestures in 2 force levels. The device uses textile electrodes, is benchmarked over a selected frequency spectrum, and uses a new drive pattern. Experimental results show that 179 kHz achieves the highest signal-to-noise ratio (SNR) and reveals the most distinct features. By analyzing the 49,920 samples from 6 participants, the device is demonstrated to have an average recognition accuracy of 98.96%. As a comparison, the medical electrodes achieved an accuracy of 98.05%.
AU - Lou,Z
AU - Min,X
AU - Li,G
AU - Avery,J
AU - Stewart,R
DO - 10.1109/JBHI.2024.3417616
EP - 5864
PY - 2024///
SN - 2168-2194
SP - 5855
TI - Advancing sensing resolution of impedance hand gesture recognition devices
T2 - IEEE Journal of Biomedical and Health Informatics
UR - http://dx.doi.org/10.1109/JBHI.2024.3417616
UR - https://ieeexplore.ieee.org/abstract/document/10568331
VL - 28
ER -