VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration
This addresses efficiency bottlenecks in activity recognition for applications like healthcare or surveillance, though it appears incremental as it builds on existing LSTM methods.
The paper tackles the challenge of processing large-scale trajectory data for activity recognition by proposing VecLSTM, a framework that enhances LSTM efficiency through vectorization layers and database integration. It achieves 85.57% validation accuracy and reduces training time by 26.2% compared to traditional LSTMs.
Activity recognition is a challenging task due to the large scale of trajectory data and the need for prompt and efficient processing. Existing methods have attempted to mitigate this problem by employing traditional LSTM architectures, but these approaches often suffer from inefficiencies in processing large datasets. In response to this challenge, we propose VecLSTM, a novel framework that enhances the performance and efficiency of LSTM-based neural networks. Unlike conventional approaches, VecLSTM incorporates vectorization layers, leveraging optimized mathematical operations to process input sequences more efficiently. We have implemented VecLSTM and incorporated it into the MySQL database. To evaluate the effectiveness of VecLSTM, we compare its performance against a conventional LSTM model using a dataset comprising 1,467,652 samples with seven unique labels. Experimental results demonstrate superior accuracy and efficiency compared to the state-of-the-art, with VecLSTM achieving a validation accuracy of 85.57\%, a test accuracy of 85.47\%, and a weighted F1-score of 0.86. Furthermore, VecLSTM significantly reduces training time, offering a 26.2\% reduction compared to traditional LSTM models.