Hierarchical sensor fusion for micro-gestures recognition with pressure sensor array and radar

Li, H., Liang, X., Shrestha, A., Liu, Y., Heidari, H. , Le Kernec, J. and Fioranelli, F. (2020) Hierarchical sensor fusion for micro-gestures recognition with pressure sensor array and radar. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 4(3), pp. 225-232. (doi: 10.1109/JERM.2019.2949456)

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Abstract

This paper presents a hierarchical sensor fusion approach for human micro-gesture recognition by combining an Ultra Wide Band (UWB) Doppler radar and wearable pressure sensors. First, the wrist-worn pressure sensor array (PSA) and Doppler radar are used to respectively identify static and dynamic gestures through a Quadratic-kernel SVM (Support Vector Machine) classifier. Then, a robust wrapper method is applied on the features from both sensors to search the optimal combination. Subsequently, two hierarchical approaches where one sensor acts as ‛enhancer‚ of the other are explored. In the first case, scores from Doppler radar related to the confidence level of its classifier and the prediction label corresponding to the posterior probabilities are utilized to maximize the static hand gestures classification performance by hierarchical combination with PSA data. In the second case, the PSA acts as an ‛Enhancer‚ for radar to improve the dynamic gesture recognition. In this regard, different weights of the ‛Enhancer‚ sensor in the fusion process have been evaluated and compared in terms of classification accuracy. A realistic cross-validation method is chosen to test one unknown participant with the model trained by data from others, demonstrating that this hierarchical fusion approach for static and dynamic gestures yields approximately 16.7% improvement in classification accuracy in the best cases.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Heidari, Dr Hadi and Le Kernec, Dr Julien and Shrestha, Mr Aman and Li, Haobo and Liang, Xiangpeng and Liu, Miss Yuchi
Authors: Li, H., Liang, X., Shrestha, A., Liu, Y., Heidari, H., Le Kernec, J., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
Publisher:IEEE
ISSN:2469-7249
ISSN (Online):2469-7257
Published Online:24 October 2019
Copyright Holders:Copyright © 2019 Crown Copyright
First Published:First published in IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology 4(3): 225-232
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy