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In the field of machine intelligence and ubiquitous computing, there has been a growing interest in human activity recognition using wearable sensors. Over the past few decades, researchers have extensively explored learning-based methods to develop effective models for identifying human behaviors. Deep learning algorithms, known for their powerful feature extraction capabilities, have played a prominent role in this area. These algorithms can conveniently extract features that enable excellent recognition performance. However, many successful deep learning approaches have been built upon complex models with multiple hyperparameters. This paper examines the current research on human activity recognition using deep learning techniques and discusses appropriate recognition strategies. Initially, we employed multiple convolutional neural networks to determine an effective architecture for human activity recognition. Subsequently, we developed a hybrid convolutional neural network that incorporates a channel attention mechanism. This mechanism enables the network to capture deep spatio-temporal characteristics in a hierarchical manner and distinguish between different human movements in everyday life. Our investigations, using the UCI-HAR, WISDM, and IM-WSHA datasets, demonstrated that our proposed model, which includes cross-channel multi-size convolution transformations, outperformed previous deep learning architectures with accuracy rates of 98.92%, 98.80%, and 98.45% respectively. These results indicate that the suggested model surpasses state-of-the-art approaches in terms of overall accuracy, as supported by the research findings. [ABSTRACT FROM AUTHOR]