A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


Citation

BibTex format

@inproceedings{Varghese:2020:10.1109/RoboSoft48309.2020.9116031,
author = {Varghese, RJ and Nguyen, A and Burdet, E and Yang, G-Z and Lo, BPL},
doi = {10.1109/RoboSoft48309.2020.9116031},
pages = {668--675},
publisher = {IEEE},
title = {Nonlinearity compensation in a multi-DoF shoulder sensing exosuit for real-time teleoperation},
url = {http://dx.doi.org/10.1109/RoboSoft48309.2020.9116031},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi- DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.
AU - Varghese,RJ
AU - Nguyen,A
AU - Burdet,E
AU - Yang,G-Z
AU - Lo,BPL
DO - 10.1109/RoboSoft48309.2020.9116031
EP - 675
PB - IEEE
PY - 2020///
SP - 668
TI - Nonlinearity compensation in a multi-DoF shoulder sensing exosuit for real-time teleoperation
UR - http://dx.doi.org/10.1109/RoboSoft48309.2020.9116031
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000610491800077&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/9116031
UR - http://hdl.handle.net/10044/1/88044
ER -