Abstract
Human activity recognition (HAR) research using Artificial Intelligence (AI) is being leveraged in many areas such as healthcare monitoring, surveillance motion tracking, sports training, and assisted living. The proposed work has developed a machine learning algorithm to analyze the data, collected in series time signals, from smartphones sensors like accelerometer and 3D gyroscope. The objective of the work is also to decrease the vast dimensions of the datasets. The activities of human are classified and predicted using a fusion of ML supervised and active learning models like logistic regression, QDA, k-nearest neighbor, support vector machine, ANN–classifier, decision tree, Naïve Bayes. The algorithm compares model scores to attain maximum efficiency through dataset training and prediction. With the proposed algorithm, precision levels >90% were obtained for six human activity.
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Vinay Kumar, A., Neeraj, M., Akash Reddy, P., Chavan, A. (2022). Machine Learning-Based Human Activity Recognition Using Smartphones. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_51
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DOI: https://doi.org/10.1007/978-981-19-0011-2_51
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