A Neural Network Pruning Approach to Human Activity Identification Based on Deep Learning

Abstract

In big data applications such as ambient healthcare-supported living, accurate human activity identification may be of tremendous use. In current years, research that is associated with the study of human behavior has been the focus of greater interest among engineers working in the field of computer vision, in particular research that makes use of deep learning. Deep learning (DL) strategies have made significant contributions to the advancement of research in HAR (Human Activity Identification). When it comes to the automated feature extraction, these deep learning algorithms perform far better than traditional machine learning (ML) approaches. In recent years, a great number of DL models have been shown to be state-of-the-art systems that can effectively identify basic and complicated human actions in order to deal with the HAR. In trained neural networks, pruning techniques of neural networks may reduce the number of available parameters, which in turn improves the computing speed of inference, all without compromising correctness of the model. By applying the neural network pruning approach to human activity identification based on deep learning, this study demonstrates its usefulness. PAMAP2 data were analyzed using an architecture called long short-term memory (LSTM), which was used to identify everyday human activities. Before training a LSTM network, the data was preprocessed. PAMAP2 is a publicly available benchmark complex activity dataset that is used to assess the proposed architecture. The results of the experiments indicate that suggested LSTM model achieves the best level of accuracy (97.42%) on the same dataset as other baseline DL models, showing a considerable improvement over other model. The excellent performance of LSTM was also validated by our investigations when comparison to the performances provided by the other base models utilised. The findings of the experiments demonstrate that the approach we have suggested is capable of accurately predicting the social actions of humans.