Search: WFRF:(Guo Jing Fang)
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Depression Prevalen...
Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality
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- Fang, Jing (author)
- Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
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- Wang, Tao (author)
- Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
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- Li, Cancheng (author)
- Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
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- Hu, Xiping (author)
- Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China;Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
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- Ngai, Edith (author)
- Uppsala universitet,Datorteknik
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- Seet, Boon-Chong (author)
- Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand
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- Cheng, Jun (author)
- Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
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- Guo, Yi (author)
- Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Neurol,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China
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- Jiang, Xin (author)
- Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Geriatr,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China
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(creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
- 2019
- English.
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 174425-174437
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Abstract
Subject headings
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- In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Annan hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Other Health Sciences (hsv//eng)
Keyword
- Depression prevalence
- depression analysis
- gait abnormality
- machine learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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