Optimized Multi-Label Human Activity Recognition with Focal Loss and Attention-Enhanced LSTM Networks

Recognizing human activity using artificial intelligence and deep learning methods has become increasingly important in various fields, including medicine, sports, security, and wearable technology. With the rise of data collection tools that utilize multidimensional sensors and the growing need for faster and more accurate analysis of human behaviors, deep neural networks can potentially improve traditional approaches, enhancing the accuracy and efficiency of activity detection systems. This research, which leverages deep learning and neural networks in the arena of human activity detection, plays a significant role in the advancement of sensor data and time series analysis models. Previous studies often converted time series data into structured formats; however, these methods mainly struggled with accuracy and efficiency in representing and analyzing complex features, which limited their practical applications. The current research aims to address this gap by focusing on deep neural network models. These models excel in processing multidimensional data and learning complex features, making them better suited for recognizing and classifying human behaviors.