ABSTRACT
With the introduction of mobile-wearable gadgets, the embedding of health-tracking capabilities has advanced dramatically. The Covid-19 years have accelerated research toward wearable sensor-based health monitoring. One of the most important applications in health monitoring is human activity identification. Daily behaviors like walking, sitting and jogging, as well as crucial activities such as falling forward and backward, provide a barrier in HAR (human action recognition) since they are semantically comparable. Previously developed deep learning algorithms have addressed some of these issues. However, these algorithms are hindered by a lack of training data. To cope with nonuniform samples in the human activities data, which can lead to overfitting results, this work introduces the synthetic minority oversampling approach. This article proposes a unique configuration of stacked convolutional neural network (CNN)-AR-DenseNet. With Bayesian optimization, the parameters of AR-DenseNet are optimally optimized. When compared to state-of-the-art stacked CNN network methods, the classification accuracy improved by up to 3.22%, with a substantial improvement of 5.8% over the standard CNN algorithm. © 2022 John Wiley & Sons, Ltd.