Human Activity Recognition Using Deep Learning Techniques: A Comprehensive Review

Human Activity Recognition (HAR) is a technology that can identify and classify human movements, such as walking or running. Deep learning models are capable of capturing complex patterns in data and adapting to new distributions, making them well-suited for HAR tasks. However, these models require significant amounts of time, resources, and data for training, which can be a challenge.
One approach to addressing this challenge is transfer learning, which involves using a pre-trained model on one task and adapting it to a new task with minimal additional training. This can help reduce the amount of time and data required for training, making it more feasible to apply deep learning models to HAR tasks.
There are two primary methods for transfer learning in HAR: maintaining the pre-trained network while updating weights, or using a pre-trained model for feature extraction and applying a classifier. These approaches can help improve the accuracy and efficiency of HAR systems.
Deep learning models excel at capturing intricate patterns in data, but their interpretability can be limited. For example, human activity recognition models may struggle to provide clear explanations for their predictions, making it difficult to understand why a particular movement was identified as a specific action.
To address this challenge, researchers have proposed various techniques, such as attention mechanisms and visualization tools, to enhance the interpretability of deep learning models. These approaches can help improve the understanding of how these models make predictions, which can be essential for applications where explainability is critical.
In summary, deep learning models are a powerful tool for human activity recognition, but their training requirements can be significant. Transfer learning provides a way to adapt these models to new tasks with minimal additional training, making them more feasible for practical applications. While the interpretability of these models can be limited, researchers have proposed various techniques to address this challenge and improve the understanding of how they work.