Authors:
David Díaz-Jiménez
1
;
Francisco Mata-Mata
1
;
José L. López
1
;
Luis G. Pérez-Cordón
1
;
José-María Serrano
1
;
Carmen Martínez-Cruz
2
;
Juana M. Morcillo-Martínez
3
;
Ángeles Verdejo-Espinosa
4
;
Juan C. Cuevas-Martínez
5
;
Raquel Viciana-Abad
5
;
Pedro J. Reche-López
5
;
José M. Pérez-Lorenzo
5
;
Juan F. Gaitán-Guerrero
1
and
Macarena Espinilla
1
Affiliations:
1
Department of Computer Science, University of Jaén, 23071, Jaén, Spain
;
2
Department of Languages and Computer Systems, University of Granada, 18071, Granada, Spain
;
3
Psycology Department, Faculty of Social Work, University of Jaén, 23071 Jaén, Spain
;
4
Electrical Engineering Department, University of Jaén, 23071 Jaén, Spain
;
5
Telecommunication Engineering Department, University of Jaén, 23071 Jaén, Spain
Keyword(s):
Human Activity Recognition, Data Labeling, Healthcare Monitoring, Internet of Things, Artificial Intelligence, Personalized Care, Nursing Home Monitoring.
Abstract:
This paper presents StreamTag, a platform designed for the efficient management of labeled data in healthcare environments, particularly for activity recognition systems in residential and nursing home settings. Human Activity Recognition (HAR) is crucial for monitoring patient behaviors and supporting personalized care, and this field has evolved significantly with advances in IoT and AI. StreamTag integrates a flexible data labeling structure and a modular architecture, enabling data collection, labeling, and secure management of activity data. The system leverages non-relational databases for scalable data handling, along with secure protocols to ensure data integrity and privacy. This work examines existing approaches in HAR, including data-driven, knowledge-based, and hybrid models, and situates StreamTag as a versatile solution that combines flexible user-controlled labeling with high adaptability for diverse healthcare contexts. Future directions are suggested for enhancing sy
stem functionality and integration with more advanced analytical tools.
(More)