Autors: Nikolov, P. M., Boumbarov, O. L., Manolova, A. H., Tonchev, K., Poulkov, V. K.
Title: Skeleton-Based Human Activity Recognition by Spatio-Temporal Representation and Convolutional Neural Networks with application to Cyber Physical Systems with Human in the Loop
Keywords: action recognition; convolutional neural network; cyber physical systems; human activity recognition; skeleton data clustering

Abstract: The developments in Cyber-physical systems where smart computers that can sense and understand human behavior will have enormous societal and economic impact facilitating various services in critical infrastructure and everyday life. CPS with Human in the Loop is a system that takes human response into consideration and human presence and behavior are key parts of the system. So these architectures of smart devices will need to interpret human action in real-time and predict humans' immediate intention in complex, noisy and cluttered environments. One of the main goals of researchers will be the development of CPS that can understand complex human activities. In this paper we propose a novel skeleton-based approach utilizing spatio-temporal information and convolutional neural networks for classification of human activities.

References

    Issue

    in Proceedings of International Conference on Telecommunications and Signal Processing (TSP), 4-6 July 2018, pp. 437-441, 2018, Greece, DOI 10.1109/TSP.2018.8441171

    Copyright IEEE

    Цитирания (Citation/s):
    1. Tehrani, B.M., Wang, J., Wang, C., "Review of Human-in-the-Loop Cyber-Physical Systems (HiLCPS): The Current Status from Human Perspective", Computing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, pp. 470-478, 2019. - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Syed, A.S., Syed, Z.S., Memon, A.K., "Continuous human activity recognition in logistics from inertial sensor data using temporal convolutions in CNN", International Journal of Advanced Computer Science and Applications, vol. 11, no. 10, pp. 597-602, 2020, DOI: 10.14569/IJACSA.2020.0111074. - 2020 - в издания, индексирани в Scopus или Web of Science
    3. Ganesan, A., Paul, A., Nagabushnam, G., & Gul, M. J. J. (2021). Human-in-the-Loop Predictive Analytics Using Statistical Learning. Journal of Healthcare Engineering, 2021. - 2021 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science