Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand.
Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand.
Math Biosci Eng. 2022 Apr 1;19(6):5671-5698. doi: 10.3934/mbe.2022265.
Currently, identification of complex human activities is experiencing exponential growth through the use of deep learning algorithms. Conventional strategies for recognizing human activity generally rely on handcrafted characteristics from heuristic processes in time and frequency domains. The advancement of deep learning algorithms has addressed most of these issues by automatically extracting features from multimodal sensors to correctly classify human physical activity. This study proposed an attention-based bidirectional gated recurrent unit as Att-BiGRU to enhance recurrent neural networks. This deep learning model allowed flexible forwarding and reverse sequences to extract temporal-dependent characteristics for efficient complex activity recognition. The retrieved temporal characteristics were then used to exemplify essential information through an attention mechanism. A human activity recognition (HAR) methodology combined with our proposed model was evaluated using the publicly available datasets containing physical activity data collected by accelerometers and gyroscopes incorporated in a wristwatch. Simulation experiments showed that attention mechanisms significantly enhanced performance in recognizing complex human activity.
目前，通过使用深度学习算法，复杂人类活动的识别正经历指数级增长。传统的识别人类活动的策略通常依赖于时间和频率域中启发式过程的手工制作特征。深度学习算法的进步通过从多模态传感器自动提取特征来正确分类人类身体活动，解决了这些问题中的大部分问题。本研究提出了一种基于注意力的双向门控循环单元（Att-BiGRU）来增强循环神经网络。这种深度学习模型允许灵活的正向和反向序列，以提取时间相关特征，从而实现高效的复杂活动识别。然后，通过注意力机制使用检索到的时间特征来举例说明重要信息。结合我们提出的模型的人类活动识别 (HAR) 方法使用包含由手表中集成的加速度计和陀螺仪收集的身体活动数据的公开数据集进行了评估。仿真实验表明，注意力机制显著提高了识别复杂人类活动的性能。
