IoT Based Human Activity Recognition System Using Smart Sensors

Sehrawat, Deepti; Gill, Nasib Singh

doi:10.25046/aj050461

Cited by 9 publications

(1 citation statement)

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“…It emphasized the potential benefits of smart energy data in supporting the health and care system, giving a complete description of the two main categories in which the research was focused on: NILM and IoT-based methods (ILM). Other approaches as [18], [19] presented a solution based on IoT, but considering wearable sensors, such as accelerometers and smart devices, and in the case of [20], the authors proposed an intrusive approach based on computer vision techniques: a background subtraction of images, followed by 3D Convolutional Neural Networks. They used a camera to record the video and a processor that performs the task of recognition, which raises privacy concerns and hence, a low opportunity for a massive adaption of the system.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

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Section: Related Workmentioning

confidence: 99%