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.

翻译：活动识别因轨迹数据规模庞大且需即时高效处理而成为一项具有挑战性的任务。现有方法尝试通过采用传统LSTM架构缓解此问题，但这些方法在处理大规模数据集时往往效率低下。针对这一挑战，我们提出VecLSTM——一种提升基于LSTM神经网络性能与效率的新型框架。与传统方法不同，VecLSTM引入向量化层，利用优化的数学运算更高效地处理输入序列。我们已实现VecLSTM并将其集成至MySQL数据库。为评估VecLSTM的有效性，我们使用包含1,467,652个样本及七类独特标签的数据集，将其性能与传统LSTM模型进行对比。实验结果表明，相较于现有最优方法，VecLSTM在准确率与效率上均表现优异：验证准确率达85.57%，测试准确率达85.47%，加权F1分数为0.86。此外，VecLSTM显著缩短训练时间，较传统LSTM模型减少26.2%。