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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8749847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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