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.


BibTex format

author = {Teachasrisaksakul, K and Wu, L and Yang, G-Z and Lo, B},
doi = {10.1109/EMBC.2018.8513098},
pages = {3517--3520},
publisher = {IEEE},
title = {Hand Gesture Recognition with Inertial Sensors.},
url = {http://dx.doi.org/10.1109/EMBC.2018.8513098},
year = {2018}

RIS format (EndNote, RefMan)

AB - Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.
AU - Teachasrisaksakul,K
AU - Wu,L
AU - Yang,G-Z
AU - Lo,B
DO - 10.1109/EMBC.2018.8513098
EP - 3520
PY - 2018///
SN - 1557-170X
SP - 3517
TI - Hand Gesture Recognition with Inertial Sensors.
UR - http://dx.doi.org/10.1109/EMBC.2018.8513098
UR - https://www.ncbi.nlm.nih.gov/pubmed/30441137
UR - http://hdl.handle.net/10044/1/64800
ER -