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Depression Identification from Gait Spectrum Features Based on Hilbert-Huang Transform
Yuan, YaHui1; Li, Baobin1; Wang, Ning2; Ye, Qing1,3; Liu, Yan1; Zhu, Tingshao4
Conference Name4th International Conference on Human Centered Computing, HCC 2018
Source PublicationHuman Centered Computing - 4th International Conference, HCC 2018, Revised Selected Papers
Conference DateDecember 5, 2018 - December 7, 2018
Conference PlaceMerida, Mexico
PublisherSpringer Verlag
Contribution Rank4

Depression is a common mental illness, which is extremely harmful to individuals and society. Timely and effective diagnosis is very important for patients’ treatments and depression preventions. In this paper, we treat the trajectory of gait as signal, proposing a new direction to detect the depression with gait frequency features based on Hilbert-Huang transform (HHT). Two groups of participants are recruited in this experiment, including 47 healthy people and 54 depressed patients, respectively. We process the gait data with HHT and build the classification models which verification method is leave-one-out. The best result of our work is 91.09% when the model I is adopted and the classifier is SVM. The corresponding specificity and sensitivity are 87.23% and 94.44% respectively. It verifies that the gait frequency of patients with depression is significantly different from that of healthy people, and the frequency domain features of gait are helpful for the diagnosis of depression. © 2019, Springer Nature Switzerland AG.

KeywordClassification models - Depression - Frequency domains - Frequency features - Gait - Hilbert Huang transforms - Kinect - Verification method
Subject AreaMathematical Transformations
Indexed ByEI
EI Accession Number20191706837407
EI KeywordsDiagnosis - Diseases - Frequency domain analysis - Spectrum analysis
EI Classification Number461.6 Medicine and Pharmacology - 921.3 Mathematical Transformations
Citation statistics
Document Type会议论文
Affiliation1.University of Chinese Academy of Sciences, Beijing, China;
2.Beijing Institute of Electronics Technology and Application, Beijing, China;
3.China Center for Modernization Research, Chinese Academy of Sciences, Beijing, China;
4.Institute of Psychology Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Yuan, YaHui,Li, Baobin,Wang, Ning,et al. Depression Identification from Gait Spectrum Features Based on Hilbert-Huang Transform[C]:Springer Verlag,2019:503-515.
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