Researcher link workshop of digital innovation for surgery planning, simulation and treatment was held in Beijing-China from 16 to 18 of October 2019.
This workshop was funded by Newton Fund, British Council and National Natural Science Foundation of China.
Academics and researcher from across the UK universities such as Imperial College London, Kings College London, Manchester University, Exeter University, Bournemouth University and so on had the opportunity to work with Academics and researchers from China to present their research, develop new ideas and investigate potentials for collaboration.
For further information, please visit https://displast.bournemouth.ac.uk/
The oral presentation of BU academic in “2019 5th International Conference on Frontiers of Signal Processing (ICFSP 2019)-France” was awarded an “Excellent Oral presentation certificate”.
Dr Roya Haratian, academic at BU Design and Engineering department of Science and Technology Faculty, presented her research paper with title of “Assistive Technology for Mental wellbeing” which is related to her ongoing research in the field of sensors, signal processing, machine learning and AI.
The paper will be published in proceedings by IEEE and will be archived into IEEE Xplore Database as well as Ei Compendex and Scopus.
The conference was held at Ecole Centrale Marseille in Marseille, France during September 18-20, 2019.
Development of intelligent devices and AI algorithms for recognition of user experience through emotion detection using physiological signals are explored in this project. The designed intelligent device would recognize user’s emotion quality and intensity in a two dimensional emotion space continuously. The continuous recognition of the user’s emotion during human-machine interaction (HMI) will enable the machine to adapt its activity based on the user’s emotion in a real-time manner, thus improving user experience.
Experience of emotion is one of the key aspects of user experience affecting to all aspects of the HMI, including utility, ease of use, and efficiency. The machine’s ability to recognize user’s emotion during user-machine interaction would improve the overall HMI usability. The machines that are aware of the user’s emotion could adapt their activity features such as speed based on user’s emotional state. This project focuses on emotion recognition through physiological signals, as this bypasses social masking and the prediction is more reliable.
Prediction of emotion through physiological signals has the advantage of elimination of social masking and making the prediction more reliable. The key advantage of this project over others presented to date is the use of the least number of modalities (only two physiological signals) to predict the quality and intensity of emotion continuously in time, and using the most recent widely accepted emotion model.
If you are interested to collaborate or know more about this project please contact Roya Haratian, lecturer in Department of Design and Engineering, Science and Technology Faculty.