Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
School of Computing, Ulster University, Shore Road, Newtownabbey, Co., Antrim BT37 0QB, Northern Ireland, UK.
Sensors (Basel). 2020 Mar 8;20(5):1479. doi: 10.3390/s20051479.
Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people's lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people's activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.
在过去的几年中，物联网 (IoT) 得到了极大的发展，智能家居设备逐渐进入人们的生活就是一个例子。为了最大限度地发挥这些部署的影响，需要进行基于家庭的活动识别，以最初识别智能家居环境中的行为，并利用这些信息提供更好的健康和社会护理服务。活动识别能够从智能家居中嵌入的传感器收集的有关人与环境交互的信息中识别人们的活动。在本文中，使用匿名二进制传感器（如压力传感器、接触传感器、被动红外传感器等）收集的二进制数据来识别活动。为了进行基于家庭的活动识别，提出了一种具有局部随机敏感自动编码器 (LiSSA) 方法的径向基函数神经网络 (RBFNN)。自动编码器 (AE) 用于通过将二进制输入转换为连续输入来从二进制传感器数据中提取有用特征，以提取更高水平的隐藏信息。通过最小化训练误差和随机灵敏度度量，增强了所提出方法的泛化能力，试图提高分类器容忍传感器数据不确定性的能力。使用四个二进制家庭活动识别数据集，包括 OrdonezA、OrdonezB、Ulster 和 van Kasteren（vanKasterenADL）的日常生活活动数据，评估所提出方法的有效性。与支持向量机 (SVM)、多层感知机神经网络 (MLPNN)、随机森林和基于 RBFNN 的方法等知名基准方法相比，所提出的方法在四个数据集上的准确率分别为 98.35%、86.26%、96.31%和 92.31%，性能最佳。
