Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Teachasrisaksakul:2018:10.1109/EMBC.2018.8513098,
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}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
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
PB  - IEEE
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  -
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