Predicting activities of daily living via temporal point processes: Approaches and experimental results. (December 2021)

• Record Type: Journal Article
• Authors: Fortino, Giancarlo; Guzzo, Antonella; Ianni, Michele; Leotta, Francesco; Mecella, Massimo
• Is Part Of: Computers & electrical engineering. Volume 96:Part B(2021)
• Publication Date: 2021-12
• Subjects: Marked temporal point processes -- Activities of daily living -- Activity prediction -- Ambient assisted living; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107567
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Activity Prediction is foreseeing the following activity people are going to execute. This is a crucial task in smart home environments, i.e., in order to facilitate the daily routines of elderly people with or without special needs. In this paper, we focused on Activity Daily Living prediction and we proposed a novel activity prediction technique based on the combination of Marked Temporal Point Processes and Neural Networks. Experiments on real and synthetic smart space datasets have shown that our approach is able to conveniently represent and predict daily living activities in an unsupervised way. We evaluated its performance and compared its results with state-of-the-art methods providing freely available implementations. Noticeably, the proposed approach outperforms the best concurrent algorithm by obtaining an improvement of F1-score of 60% (on average of the considered datasets).