Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Bruna Maria Vittoria Guerra; Emanuele Torti; Elisa Marenzi; Micaela Schmid; Stefano Ramat; Francesco Leporati; Giovanni Danese

doi:10.3389/fnins.2023.1256682

Frontiers in Neuroscience (Oct 2023)

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Bruna Maria Vittoria Guerra,
Emanuele Torti,
Elisa Marenzi,
Micaela Schmid,
Stefano Ramat,
Francesco Leporati,
Giovanni Danese

Affiliations

Bruna Maria Vittoria Guerra: Bioengineering Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Emanuele Torti: Custom Computing and Programmable Systems Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Elisa Marenzi: Custom Computing and Programmable Systems Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Micaela Schmid: Bioengineering Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Stefano Ramat: Bioengineering Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Francesco Leporati: Custom Computing and Programmable Systems Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Giovanni Danese: Custom Computing and Programmable Systems Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy

DOI: https://doi.org/10.3389/fnins.2023.1256682
Journal volume & issue: Vol. 17

Abstract

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Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

Published in Frontiers in Neuroscience

ISSN: 1662-4548 (Print); 1662-453X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: http://www.frontiersin.org/neuroscience

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Abstract

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