A Novel CNN, Bidirectional Long-Short Term Memory, and Gated Recurrent Unit-Based Hybrid Approach for Human Activity Recognition

Abstract

Human activity recognition (HAR) is a crucial and challenging classification task for a range of applications from surveillance to assistance. Existing sensor-based HAR systems have limited training data availability and lack fast and accurate methods for robust and rapid activity recognition. In this paper, a novel hybrid HAR technique based on CNN, bi-directional long short-term memory, and gated recurrent units is proposed that can accurately and quickly recognize new human activities with a limited training set and high accuracy. The experiment was conducted on UCI Machine Learning Repository's MHEALTH dataset to analyze the effectiveness of the proposed method. The confusion matrix and accuracy score are utilized to gauge the performance of the presented model. Experiments indicate that the proposed hybrid approach for human activity recognition integrating CNN, bi-directional LSTM, and gated recurrent outperforms computing complexity and efficiency. The overall findings demonstrate that the proposed hybrid model performs exceptionally well, with enhanced accuracy of 94.68%.