Research output per year
Research output per year
Personal profile
Biography
Dr Ghazal Bargshady is a researcher in Affective Computing and AI in healthcare. She was awarded her PhD in Artificial Intelligence and Computer Vision focusing on deep learning in pain recognition from facial expressions at the University of Southern Queensland, Springfield, in December 2020. From 2021 to 2023, she continued her research as a postdoctoral research fellow at the University of Canberra, contributing to a depression recognition project using facial expression analysis.
Dr Bargshady’s work spans several cutting-edge projects at the intersection of AI, affective computing, and healthcare. These include automated pain assessment using facial video and fNIRS signals, depression detection through visual cues, radiology image analysis, and driver affect recognition using multimodal biosignals and computer vision technologies.
Her research interests include computer vision, human factors, brain–computer interfaces, affective computing, biosignal processing, wearable sensors, multimodal data fusion, deep learning, and facial expression analysis.
Passionate about education and innovation, Dr Bargshady is dedicated to mentoring emerging researchers in AI and intelligent computational modeling, with a focus on applications in healthcare and safety.
Current Active Project for Supervision of PhDs and Master by Research
- Driver's Distraction, Stress, Fatigue Recognition for Road Safety.
- Computer Vision, BioSignals, NeuroSignals in AI and Healthcare
- Human Factor in Health and Safety
- Wearable Sensors
- Deep Learning and Multimodal Fusioning Modeling
Teaching Activities:
Ghazal has been lecturer, unit conviner, unit moderator for the following units at UC:
- Programming for Data Science G (11521),
- Data Analytics and Business Intelligence (8096/8097),
- Soft Computing (7168)
- Computer Vision(11376/8890)
- Software Technology 2 (7170)
- Introduction to Data Science G (11516)
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
PhD, Enhanced deep learning prediction modelling to detect pain from facial expressions video images, University of Southern Queensland
10 Jul 2017 → 10 Dec 2020
Award Date: 10 Dec 2020
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Collaborations and top research areas from the last five years
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Research output
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Estimating Depression Severity from Long-Sequence Face Videos via an Ensemble Global Diverse Convolutional Model
Bargshady, G. & Goecke, R., 29 Jan 2024, 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2023): Techniques and Applications, DICTA 2023. UIhaq, A., Torr, P., Paul, M., Walsh, T., Chakraborty, S., Islam, S. & Razzak, I. (eds.). Los Alamitos (CA), USA: IEEE, Institute of Electrical and Electronics Engineers, p. 296-303 8 p. (2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023).Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review
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An Investigation of Video Vision Transformers for Depression Severity Estimation from Facial Video Data
Bargshady, G. & Goecke, R., 2024, Image and Video Technology - 11th Pacific-Rim Symposium, PSIVT 2023, Proceedings: 11th Pacific-Rim Symposium, PSIVT 2023, Auckland, New Zealand, November 22–24, 2023, Proceedings. Yan, W. Q., Nguyen, M., Nand, P. & Li, X. (eds.). Singapore: Springer, p. 211–220 10 p. (Lecture Notes in Computer Science (LNCS); vol. 14403).Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review
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Enhanced deep learning algorithm development to detect pain intensity from facial expression images
Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F. & Wang, H., 1 Jul 2020, In: Expert Systems with Applications. 149, p. 1-10 10 p., 113305.Research output: Contribution to journal › Article › peer-review
142 Citations (Scopus) -
The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space
Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F. & Wang, H., Dec 2020, In: Applied Soft Computing Journal. 97, p. 1-14 14 p., 106805.Research output: Contribution to journal › Article › peer-review
43 Citations (Scopus) -
Ensemble neural network approach detecting pain intensity from facial expressions
Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F. & Wang, H., Sept 2020, In: Artificial Intelligence in Medicine. 109, p. 1-12 12 p., 101954.Research output: Contribution to journal › Article › peer-review
56 Citations (Scopus)