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Advances in sensor technology have led to a surge of interest in recognizing human activities based on sensor data. This recognition, known as Human Activity Recognition (HAR), has wide-ranging applications in everyday life, such as medical care, movement analysis, intelligent monitoring systems, and smart homes.

HAR can be categorized into two main classes: video-based and sensor-based. Video-based HAR systems rely on cameras to capture videos and images and utilize computer vision technology to identify human actions. However, these systems are susceptible to environmental factors and privacy concerns. In contrast, sensor-based systems use environmental or wearable sensors embedded in smart devices like smartphones and smartwatches to determine human actions.

Wearable sensors present a complex challenge in HAR due to the classification of time-series data with multiple variables. Traditional machine learning algorithms have been successful in categorizing human behaviors, but manual feature extraction requires specialized knowledge, limiting its practicality. Deep learning models, particularly convolutional neural networks (CNN), have revolutionized HAR by automating the feature extraction process.

CNN models have proven effective in extracting features and achieving accuracy in sensor-based HAR. The combination of CNN and recurrent neural networks (RNN) allows for a comprehensive representation of spatial and temporal features. To enhance the effectiveness of HAR, the squeeze-and-excitation (SE) block acts as a channel-attention mechanism to prioritize valuable feature maps while suppressing unreliable ones.

In this study, a novel approach called ResNet-BiGRU-SE is proposed, combining a hybrid CNN with a channel attention system for human activity recognition. Experiments using standard datasets demonstrated that the proposed model outperforms previous deep learning architectures in terms of accuracy.

The utilization of sensor-based HAR holds immense potential in various domains, such as healthcare, sports analysis, surveillance systems, and human-robot interactions. It enables advanced movement tracking systems, automatic interpretation of player actions, user identification in surveillance, and gesture recognition.

Harnessing the power of sensor-based HAR can bring significant advantages and advancements to these diverse sectors. The proposed model presents a promising solution for accurately identifying and predicting human behaviors based on sensor data.

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Human Activity Recognition Using Deep Learning Techniques - Fagen wasanni