With the widespread application of mobile edge computing (MEC), MEC is serving as a bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also moving toward supervising individual health in an automatic and intelligent manner. One of the main MEC technologies in healthcare monitoring systems is human activity recognition (HAR). Built-in multifunctional sensors make smartphones a ubiquitous platform for acquiring and analyzing data, thus making it possible for smartphones to perform HAR. The task of recognizing human activity using a smartphone’s built-in accelerometer has been well resolved, but in practice, with the multimodal and high-dimensional sensor data, these traditional methods fail to identify complicated and real-time human activities. This paper designs a smartphone inertial accelerometer-based architecture for HAR. When the participants perform typical daily activities, the smartphone collects the sensory data sequence, extracts the high-efficiency features from the original data, and then obtains the user’s physical behavior data through multiple three-axis accelerometers. The data are preprocessed by denoising, normalization and segmentation to extract valuable feature vectors. In addition, a real-time human activity classification method based on a convolutional neural network (CNN) is proposed, which uses a CNN for local feature extraction. Finally, CNN, LSTM, BLSTM, MLP and SVM models are utilized on the UCI and Pamap2 datasets. We explore how to train deep learning methods and demonstrate how the proposed method outperforms the others on two large public datasets: UCI and Pamap2.

中文翻译：

---

智能手机实时人类活动识别的深度学习模型

随着移动边缘计算（MEC）的广泛应用，MEC充当了缩小医护人员与患者之间差距的桥梁。与此相关的是，MEC也正朝着以自动和智能的方式监督个人健康的方向发展。医疗活动监控系统中的主要MEC技术之一是人类活动识别（HAR）。内置多功能传感器使智能手机成为获取和分析数据的无处不在的平台，从而使智能手机能够执行HAR。使用智能手机的内置加速度计识别人类活动的任务已经得到很好的解决，但是在实践中，利用多模式和高维传感器数据，这些传统方法无法识别复杂且实时的人类活动。本文设计了一种用于HAR的基于智能手机惯性加速度计的架构。当参与者进行日常日常活动时，智能手机会收集感官数据序列，从原始数据中提取高效功能，然后通过多个三轴加速度计获得用户的身体行为数据。通过对信号进行去噪，归一化和分段处理，以提取有价值的特征向量。此外，提出了一种基于卷积神经网络的实时人类活动分类方法，该方法利用卷积神经网络进行局部特征提取。最后，在UCI和Pamap2数据集上使用了CNN，LSTM，BLSTM，MLP和SVM模型。我们探索如何训练深度学习方法，并在两种大型公共数据集上论证所提出的方法如何胜过其他方法：