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Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have bee...

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Autores principales: Bhatia, Yajurv, Bari, ASM Hossain, Hsu, Gee-Sern Jison, Gavrilova, Marina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749847/
https://www.ncbi.nlm.nih.gov/pubmed/35009944
http://dx.doi.org/10.3390/s22010403
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author Bhatia, Yajurv
Bari, ASM Hossain
Hsu, Gee-Sern Jison
Gavrilova, Marina
author_facet Bhatia, Yajurv
Bari, ASM Hossain
Hsu, Gee-Sern Jison
Gavrilova, Marina
author_sort Bhatia, Yajurv
collection PubMed
description Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance.
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spelling pubmed-87498472022-01-12 Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network Bhatia, Yajurv Bari, ASM Hossain Hsu, Gee-Sern Jison Gavrilova, Marina Sensors (Basel) Article Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance. MDPI 2022-01-05 /pmc/articles/PMC8749847/ /pubmed/35009944 http://dx.doi.org/10.3390/s22010403 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bhatia, Yajurv
Bari, ASM Hossain
Hsu, Gee-Sern Jison
Gavrilova, Marina
Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title_full Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title_fullStr Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title_full_unstemmed Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title_short Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
title_sort motion capture sensor-based emotion recognition using a bi-modular sequential neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749847/
https://www.ncbi.nlm.nih.gov/pubmed/35009944
http://dx.doi.org/10.3390/s22010403
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