Academic News

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My paper titled Emotion Recognition From Full-Body Motion Using Multiscale Spatio-Temporal Network has been accepted for publication by IEEE Transactions on Affective Computing.

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My research project has been successfully approved by the 2022 Tianjin Postgraduate Research and Innovation Project. This is my first experience of leading a provincial-level project.

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My paper titled Depression recognition using a proposed speech chain model fusing speech production and perception features has been accepted for publication by Journal of Affective Disorders.

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Recently, I have received an invitation to peer review an article titled “Single-Sensor EEG-based Depression Diagnosis” for IEEE Sensors Journal.

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I presented a talk at the 2020 IEEE HEALTHCOM Conference on 1 March 2021, entitled “A Novel Gait Analysis Method Based on The Pseudo-velocity Model for Depression Detection”.

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My paper titled A Gait Assessment Framework for Depression Detection Using Kinect Sensors has been accepted for publication by IEEE Sensors Journal.

Recent Publications

Numbers: 10| Citations: 124 | h-index: 4 | i10-index: 3

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Accomplishments

National Scholarship for Study Abroad (National Level)

China Scholarship Council (CSC) Jul 2023

Merit Student in Tianjin University (University Level)

Tianjin University Oct 2022

National Graduate Scholarship (National Level)

Ministry of Education of the People’s Republic of China Oct 2020

Second Prize in the 13th China Undergraduate Electronic Design Contest (National Level)

Chinese Institute of Electronics Dec 2017

Excellent Student Leader (Provincial Level)

The Education Department of Henan Province Jul 2017

National Encouragement Scholarship (National Level)

Ministry of Education of the People’s Republic of China Nov 2015

Projects

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This project constructs a multiscale spatio-temporal network for emotion recognition based on full-body motion.

We propose a novel machine speech chain model for depression recognition (MSCDR).

In this project, we propose a novel gait assessment framework to implement non-intrusive, real-time and automatic depression detection using Kinect.

In this project, we used the fNIRS method to collect the resting state data of MDD, SCZ, and HC for the PFC region.

This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS).

Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigated changes in gait patterns of depressed patients.

Contact

Looking forward to collaborating with other scholars