Abstract
In the field of Human Activity Recognition (HAR), the precise identification of human activities from time-series sensor data is a complex yet vital task, given its extensive applications across various industries. This study introduces an advanced HAR technique that markedly improves activity recognition by combining multimodal sensor fusion with a transformer-based attention mechanism. Our methodology begins with rigorous preprocessing of the raw data from multiple sensors, focusing on cleaning and normalizing the data to create ideal conditions for subsequent analysis. We then apply an innovative sensor fusion strategy alongside a transformer-based attention mechanism to accurately and comprehensively detect human activities. The effectiveness of our method was rigorously evaluated on two widely recognized benchmark datasets in HAR research, Extrasensory and UCI-HAR, both known for their complexity and broad usage. The evaluation concentrates on the model’s ability to precisely classify primary human activities, particularly in near-real-time situations. The findings demonstrate that our model excels in accuracy and adeptly identifies primary human activities, marking a notable progression in HAR technology. This enhancement highlights the strong potential of our proposed approach, combining sensor fusion and attention mechanisms to advance activity recognition in real-life settings.