Research Assistant Professor
PolyU Scholars Hub
Dr Joni ZHONG
Research Assistant Professor
BEng (SCUT), MPhil (PolyU), PhD (UHH), SMIEEE
- ST516
- +852 2766 6764
- joni.zhong@polyu.edu.hk
- Assistive Technology
ORCID ID
SCOPUS IDBiography
Dr Joni Zhong graduated from the South China University of Technology (Guangzhou, China) with double bachelor’s degrees in control science and computer science. He then obtained his MPhil at The Hong Kong Polytechnic University and PhD at the University of Hamburg, Germany. Before returning to PolyU, he had been a researcher at the University of Hertfordshire (UK), the University of Plymouth (UK), Waseda University (Japan), the National Institute of Advanced Industrial Science and Technology (Japan), etc, where he established close interdisciplinary collaborations with researchers in neuroscience, cognitive sciences and philosophy from the EU, Japan and the UK.
Dr Zhong had received a 3-year Marie-Curie fellowship for his PhD study. He had also received such awards as the Best Student Paper at ICANN 2011, the Best Theory Paper at ICMIC 2017 and the Best Paper Nomination at Cyber 2019. He had organised a few special sessions and workshops at IEEE SMC 2020, IEEE ICDL-EpiRob 2020, ICIRA 2019, etc, and been a guest editor of several journals such as IEEE Transactions on Cognitive and Developmental Systems, Interaction Studies, Journal of Ambient Intelligence and Humanized Computing, etc.
His research interests centre around assistive technologies based on wearable devices, IoT devices and robotics. Theoretically, he particularly advocates the involvement of a human in the learning loop (i.e. human-in-the-loop learning) and predictive modelling. His current research focuses on cognitive assistive robots and their usage in elderly healthcare and wellbeing.
Dr Zhong had received a 3-year Marie-Curie fellowship for his PhD study. He had also received such awards as the Best Student Paper at ICANN 2011, the Best Theory Paper at ICMIC 2017 and the Best Paper Nomination at Cyber 2019. He had organised a few special sessions and workshops at IEEE SMC 2020, IEEE ICDL-EpiRob 2020, ICIRA 2019, etc, and been a guest editor of several journals such as IEEE Transactions on Cognitive and Developmental Systems, Interaction Studies, Journal of Ambient Intelligence and Humanized Computing, etc.
His research interests centre around assistive technologies based on wearable devices, IoT devices and robotics. Theoretically, he particularly advocates the involvement of a human in the learning loop (i.e. human-in-the-loop learning) and predictive modelling. His current research focuses on cognitive assistive robots and their usage in elderly healthcare and wellbeing.
Education and Academic Qualifications
- Master of Philosophy, The Hong Kong Polytechnic University
- Doctor of Natural Sciences, University of Hamburg
Research Interests
Research Output
- Li, J., Zhong, J., & Wang, M. (2020). An unsupervised recurrent neural network with parametric bias framework for human emotion recognition with multimodal sensor data fusion. Sensors and Materials, 32(4), 1261-1277.
- Li, J., Zhong, J., Yang, J., & Yang, C. (2020). An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion. Frontiers in Neurorobotics, 14, 55.
- Li, X., & Zhong, J. (2020). Upper limb rehabilitation robot system based on internet of things remote control. IEEE Access, 8, 154461-154470.
- Naser, A., Lotfi, A., & Zhong, J. (2020). Adaptive thermal sensor array placement for human segmentation and occupancy estimation. IEEE Sensors Journal. doi: 10.1109/JSEN.2020.3020401.
- Li, Y., Zhou, X., Zhong, J., & Li, X. (2019). Robotic impedance learning for robot-assisted physical training. Frontiers in Robotics and AI, 6, 78.
- Zeng, C., Yang, C., Zhong, J., & Zhang, J. (2019). Encoding multiple sensor data for robotic learning skills from multimodal demonstration. IEEE Access, 7, 145604-145613.
- Zhong, J., Ogata, T., Cangelosi, A., & Yang, C. (2019). Disentanglement in conceptual space during sensorimotor interaction. IET Cognitive Computation and Systems, 1(4), 103-112.
