Abstract
In recent years, with the development of IoT technologies, biological information of users has been easily acquired and accumulated. It is required that user's cognitive or emotional state could be accurately identified from such biological information. Various researches have used machine learning or deep learning however, generalized methods to handle biological data have not been established. In this paper, we propose a more versatile method using convolution neural network of deep learning to extract features from individual biometric information which is multi-channels time-series data representing user’s cognitive. We demonstrate a new method of extraction and distinguishment of features from cerebral blood flow, skin temperature, respiration and skin conductance measured during specific task conditions in the stroop experiment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: The Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.