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- Publisher Website: 10.1109/TCSS.2022.3223251
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Article: Emotion Recognition From Gait Analyses: Current Research and Future Directions
Title | Emotion Recognition From Gait Analyses: Current Research and Future Directions |
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Authors | |
Keywords | Appraisal Brain modeling Emotion recognition Emotion recognition Foot gait analysis intelligent computation Legged locomotion Observers Task analysis |
Issue Date | 5-Dec-2022 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Computational Social Systems, 2022 How to Cite? |
Abstract | Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data. |
Persistent Identifier | http://hdl.handle.net/10722/331383 |
ISSN | 2021 Impact Factor: 4.747 2020 SCImago Journal Rankings: 0.783 |
DC Field | Value | Language |
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dc.contributor.author | Xu, SH | - |
dc.contributor.author | Fang, J | - |
dc.contributor.author | Hu, XP | - |
dc.contributor.author | Ngai, E | - |
dc.contributor.author | Wang, W | - |
dc.contributor.author | Guo, Y | - |
dc.contributor.author | Leung, VCM | - |
dc.date.accessioned | 2023-09-21T06:55:15Z | - |
dc.date.available | 2023-09-21T06:55:15Z | - |
dc.date.issued | 2022-12-05 | - |
dc.identifier.citation | IEEE Transactions on Computational Social Systems, 2022 | - |
dc.identifier.issn | 2329-924X | - |
dc.identifier.uri | http://hdl.handle.net/10722/331383 | - |
dc.description.abstract | <p></p><p>Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.<br></p> | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Computational Social Systems | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Appraisal | - |
dc.subject | Brain modeling | - |
dc.subject | Emotion recognition | - |
dc.subject | Emotion recognition | - |
dc.subject | Foot | - |
dc.subject | gait analysis | - |
dc.subject | intelligent computation | - |
dc.subject | Legged locomotion | - |
dc.subject | Observers | - |
dc.subject | Task analysis | - |
dc.title | Emotion Recognition From Gait Analyses: Current Research and Future Directions | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TCSS.2022.3223251 | - |
dc.identifier.scopus | eid_2-s2.0-85144768248 | - |
dc.identifier.eissn | 2329-924X | - |
dc.identifier.issnl | 2329-924X | - |