Abstract
The need for automation is exponentially growing in this digital world. The automated detection of human activity has shown profound influence in varied mundane applications in the field of defense, patient monitoring, public security, and computer vision while imparting artificial intelligence. The intent of this work is to analyze the performance of different deep learning algorithms like logistic regression, random forest, decision tree, linear SVC, kernel SVM, and gradient boosted decision tree with grid search for the detection of basic human activities like laying, sitting, standing, walking, walking_upstairs, and walking_downstairs. An experimental set-up made for doing human activity recognition and a comprehensive comparative analysis of results is done. After applying suitable deep learning algorithms, a scrutiny was done by testing the system. The publicly available datasets are used for evaluation of human activity after a significant exploratory data analysis.
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Ramachandran, V., Rani, P.J., Alla, K. (2021). Human Action Detection Using Deep Learning Techniques. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_50
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DOI: https://doi.org/10.1007/978-981-15-7106-0_50
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