Cited by 71Emotion and Mood RecognitionGait Recognition and AnalysisHuman Pose and Action Recognition

Emotion recognition using Kinect motion capture data of human gaits

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Li, Shun, Cui, Liqing, Zhu, Changye, Li, Baobin, Zhao, Nan, Zhu, Tingshao·2016·PeerJ

Abstract

Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.

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  1. Alm CO Roth D Sproat R 2005 Emotions from text: machine learning for text-based emotion prediction Proceedings of the conference on human language technology and empirical methods in natural language processing Association for Computational Linguistics 579 586

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Authors
Li, S., Cui, L., Zhu, C., Li, B., Zhao, N., & Zhu, T.
Affiliations
  • Chinese Academy of Sciences (CN)
  • University of Chinese Academy of Sciences (CN)
Corresponding author
  • Zhao, Nan
  • Zhu, Tingshao
Venue
PeerJ
Publisher
PeerJ
Published
2016-09-15
Volume
4
Pages
e2364
ISSN
2167-8359 (ISSN-L)
OpenAlex
W2519256581
Cited by
71
References
49
Citations by year
2026: 1 · 2025: 4 · 2024: 10 · 2023: 9 · 2022: 15 · 2021: 7 · 2020: 7 · 2019: 9 · 2018: 6 · 2017: 3
FWCI
8.54
Type
journal article
Language
en
Open access
Locations
  • PeerJ (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Basic Research Program of China (2014CB744600)
  • Key Research Program of Chinese Academy of Sciences (CAC) (KJZD-EWL04)
  • CAS Strategic Priority Research Program (XDA06030800)
  • Scientific Foundation of Institute of Psychology, CAS (Y4CX143005)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Keywords
emotion recognition, human walking, Kinect motion capture, gait feature extraction, Fourier transform, principal component analysis

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