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{Rother:2019,
author = {Rother, R and Sun, Y and Lo, B},
title = {Internet of things based pervasive sensing of psychological anxiety via wearable devices under naturalistic settings},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Psychological anxiety is highly prevalent in dementia patients, and reduces the quality of life of the afflicted and their caregivers. Still, not much technological research has been conducted to address this issue in the field. This study aimed to develop a wearable system which could detect anxiety in dementia patients under naturalistic settings, and alert caregivers via a web application. The wearable system designed included an accelerometer, pulse sensor, skin conductivity sensor, and a camera. The readings would be fed into a machine learning model to output an anxiety state prediction. One participant was trialled under both controlled and naturalistic settings. The model achieved classification accuracy of 0.95 under controlled settings and 0.82 under naturalistic settings. Implications of the study include achieving relatively high classification accuracy under naturalistic settings, and the novel discovery of movement as a potential predictor of anxiety states.
AU - Rother,R
AU - Sun,Y
AU - Lo,B
PY - 2019///
TI - Internet of things based pervasive sensing of psychological anxiety via wearable devices under naturalistic settings
ER -