Department of Electrical Engineering, Netaji Subhas University of Technology, New Delhi, India.
Department of Computer Science, MSCW, University of Delhi, New Delhi, India.
ISA Trans. 2023 Jan;132:94-108. doi: 10.1016/j.isatra.2022.10.034. Epub 2022 Nov 1.
Human activity recognition can deduce the behaviour of one or more people from a set of sensor measurements. Despite its widespread applications in monitoring activities, robotics, and visual surveillance, accurate, meticulous, precise and efficient human action recognition remains a challenging research area. As human beings are moving towards the establishment of a smarter planet, human action recognition using ambient intelligence has become an area of huge potential. This work presents a method based on Bi-Convolutional Recurrent Neural Network (Bi-CRNN) -based Feature Extraction and then Random Forest classification for achieving outcomes utilizing Ambient Intelligence that are at the cutting edge of human action recognition for Autonomous Robots. The auto fusion technique used has improved fusion for utilizing and processing data from various sensors. This paper has drawn comparisons with already existing algorithms for Human Action Recognition (HAR) and tried to propose a heuristic and constructive hybrid deep learning-based algorithm with an accuracy of 94.7%.
人体活动识别可以从一组传感器测量中推断出一个或多个人的行为。尽管它在活动监测、机器人技术和视觉监控等方面有着广泛的应用，但准确、细致、精确和高效的人体动作识别仍然是一个具有挑战性的研究领域。随着人类迈向更智能的星球，利用环境智能进行人体动作识别已经成为一个极具潜力的领域。本工作提出了一种基于双卷积递归神经网络（Bi-CRNN）的特征提取方法，然后是随机森林分类方法，旨在利用自主机器人的人体动作识别的前沿技术，实现环境智能的成果。所使用的自动融合技术改进了对来自各种传感器的数据的利用和处理。本文与现有的人机交互识别（HAR）算法进行了比较，并尝试提出一种启发式和建设性的混合深度学习算法，准确率达到 94.7%。
