Motion Signal-Based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., Enhance CNN, LR, RF, DT, KNN, and SVM) approaches. This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach.