Human Daily Activities: From Detection to Prediction
Azade Fotouhi, Hamoud Guicheniti, Hussein Chour, Mouna Benmabrouk · 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO) · 2022
While the number of connected objects in the world of Internet of Thing (IoT) is increasing, an efficient and intelligent solution to exploit the huge amount of generated data from home appliances does not exist. Smart homes powered by IoT devices are able to automate and monitor the every day activities of home owners, and improve the life quality especially for elderly and disabled people. In this paper, we take advantage of deep learning and machine learning techniques and propose a comprehensive solution to tackle this problem. Our proposed approach is able to analyze raw and unlabeled IoT data from the connected devices in a smart home and provide data-driven services. After analysing and processing raw data, and then human activity detection and recognition in real time, we predict the upcoming activity and its occurring time using LSTM (Long Short-Term Memory). Our simulation results show that the developed models can predict a wide range of human activities from unlabeled data with the accuracy of 85%.