Identifying stress scores from gait biometrics captured using a camera : A cross-sectional study
Copyright © 2024 Elsevier B.V. All rights reserved..
BACKGROUND: Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner.
RESEARCH QUESTION: This study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach.
METHODS: One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants' stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels.
RESULTS: The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs.
SIGNIFICANCE: The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:109 |
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| Enthalten in: |
Gait & posture - 109(2024) vom: 11. März, Seite 15-21 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Jingying [VerfasserIn] |
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| Links: |
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| Themen: |
Gait |
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| Anmerkungen: |
Date Completed 15.03.2024 Date Revised 15.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.gaitpost.2024.01.013 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367324547 |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner | ||
| 520 | |a RESEARCH QUESTION: This study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach | ||
| 520 | |a METHODS: One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants' stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels | ||
| 520 | |a RESULTS: The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs | ||
| 520 | |a SIGNIFICANCE: The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Gait | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Perceived stress | |
| 650 | 4 | |a Regression | |
| 650 | 4 | |a Video analysis | |
| 700 | 1 | |a Wen, Yeye |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Junhong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Nan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Tingshao |e verfasserin |4 aut | |
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