[u' @article{almeida_comparative_2022, title = {A {Comparative} {Analysis} of {Human} {Behavior} {Prediction} {Approaches} in {Intelligent} {Environments}}, volume = {22}, issn = {1424-8220}, url = {https://www.mdpi.com/1424-8220/22/3/701}, doi = {https://doi.org/10.3390/s22030701}, abstract = {Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.}, language = {English}, number = {3}, journal = {Sensors}, author = {Almeida, Aitor and Bermejo, Unai and Bilbao Jayo, Aritz and Azkune, Gorka and Aguilera, Unai and Emaldi, Mikel and Dornaika, Fadi and Arganda-Carreras, Ignacio}, month = jan, year = {2022}, keywords = {AI for health, CNN, JCR3.576, LSTM, Q1, activity recognition, artificial intelligence, attention, behavior modelling, behaviour prediction, convolutional networks, embeddings, futuraal, geometric deep learning, graph neural networks, knowledge graphs, machine learning, recurrent neural networks, transformers}, pages = {701}, } ']