@article{ref1,
title="Self-esteem recognition based on gait pattern using Kinect",
journal="Gait and posture",
year="2017",
author="Sun, Bingli and Zhang, Zhan and Liu, Xingyun and Hu, Bin and Zhu, Tingshao",
volume="58",
number="",
pages="428-432",
abstract="BACKGROUND: Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem. <br><br>METHODS: 178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score. After completing the RRS, each participant walks for two minutes naturally on a rectangular red carpet, and the gait data are recorded using Kinect sensor. After data preprocessing, we extract a few behavioral features to train predicting model by machine learning. Based on these features, we build predicting models to recognize self-esteem. <br><br>RESULTS: For self-esteem prediction, the best correlation coefficient between predicted score and self-report score is 0.45 (p<0.001). We divide the participants according to gender, and for males, the correlation coefficient is 0.43 (p<0.001), for females, it is 0.59 (p<0.001). <br><br>CONCLUSION: Using gait data captured by Kinect sensor, we find that the gait pattern could be used to recognize self-esteem with a fairly good criterion validity. The gait predicting model can be taken as a good supplementary method to measure self-esteem.<br><br>Copyright © 2017 Elsevier B.V. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0966-6362",
doi="10.1016/j.gaitpost.2017.09.001",
url="http://dx.doi.org/10.1016/j.gaitpost.2017.09.001"
}