Emotion recognition using Kinect motion capture data of human gaits
Open accessAbstract
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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Li et al. achieved the highest accuracy of 80.5%.
“The highest accuracy achieved by Li et al. was 80.5% [62].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [62]
Human gait reflects the walker's emotional state.
“They showed that human gait reflects the walker's emotional state.”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [62]
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EmotionVibe: Human Emotion Recognition Through Footstep-Induced Floor Vibrations
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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)
- PMC
- PMC5028730
- 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
- gold
- doi.org
- 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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