College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel). 2021 Mar 28;21(7):2359. doi: 10.3390/s21072359.
Human activity recognition (HAR) has been a vital human-computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodology equipped with low-cost sensors is proposed for high-accuracy device-free human activity recognition. ESP8266, as the sensing hardware, was utilized to deploy the WiFi sensor network and collect multi-dimensional received signal strength indicator (RSSI) records. The proposed learning model presents a coarse-to-fine hierarchical classification framework with two-level perception modules. In the coarse-level stage, twelve statistical features of time-frequency domains were extracted from the RSSI measurements filtered by a butterworth low-pass filter, and a support vector machine (SVM) model was employed to quickly recognize the basic human activities by classifying the signal statistical features. In the fine-level stage, the gated recurrent unit (GRU), a representative type of recurrent neural network (RNN), was applied to address issues of the confused recognition of similar activities. The GRU model can realize automatic multi-level feature extraction from the RSSI measurements and accurately discriminate the similar activities. The experimental results show that the proposed approach achieved recognition accuracies of 96.45% and 94.59% for six types of activities in two different environments and performed better compared the traditional pattern-based methods. The proposed hierarchical learning method provides a low-cost sensor-based HAR framework to enhance the recognition accuracy and modeling efficiency.
人体活动识别 (HAR) 一直是智能家居中重要的人机交互服务。由于人体动作的多样性和相似性，它仍然是一项具有挑战性的任务。在本文中，提出了一种基于分层深度学习的新方法，该方法配备了低成本传感器，可用于高精度的无设备人体活动识别。ESP8266 用作传感硬件，用于部署 WiFi 传感器网络并收集多维接收信号强度指示 (RSSI) 记录。所提出的学习模型提出了一个粗到精的分层分类框架，具有两级感知模块。在粗级阶段，从 RSSI 测量值中提取了时频域的十二个统计特征，并通过巴特沃斯低通滤波器对其进行滤波，然后使用支持向量机 (SVM) 模型通过对信号统计特征进行分类来快速识别基本的人体活动。在精细阶段，门控循环单元 (GRU)，一种代表性的循环神经网络 (RNN) 类型，被应用于解决类似活动识别混乱的问题。GRU 模型可以从 RSSI 测量值中自动提取多层次特征，并准确区分相似活动。实验结果表明，所提出的方法在两种不同环境下对六种类型的活动的识别准确率分别达到 96.45%和 94.59%，优于传统的基于模式的方法。所提出的分层学习方法提供了一种基于低成本传感器的 HAR 框架，可提高识别精度和建模效率。
