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Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's pp 685–713Cite as

A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1132))

Abstract

Smart environments and mobile devices are two technologies that when combined may allow the recognition of Activities of Daily Living (ADL) and its environments. This paper focuses on the literature review of the existing machine learning methods for the recognition of ADL and its environments, by means of comparison jointly with a proposal of a novel taxonomy in this context. The sensors used for this purpose depends on the nature of the system and the ADL to recognize. The available in the mobile devices are mainly motion, magnetic and location sensors, but the sensors available in the smart environments may have different types. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment.

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Acknowledgments

This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020). This article/publication is based on work from COST Action IC1303 - AAPELE - Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226 - SHELD-ON - Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu.

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Authors and Affiliations

  1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Ivan Miguel Pires, Gonçalo Marques, Nuno M. Garcia & Nuno Pombo

  2. Altranportugal, Lisbon, Portugal

    Ivan Miguel Pires

  3. Polytechnic Institute of Viseu, Viseu, Portugal

    Ivan Miguel Pires

  4. Polytechnic Institute of Guarda, Guarda, Portugal

    Gonçalo Marques

  5. Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal

    Nuno M. Garcia

  6. Department of Computer Technology, Universidad de Alicante, San Vicente del Raspeig, Spain

    Francisco Flórez-Revuelta

  7. Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, Skopje, Macedonia

    Eftim Zdravevski

  8. Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

    Susanna Spinsante

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  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

  2. Department of Computer Sciences, Purdue University, West Lafayette, IN, USA

    Bharat K. Bhargava

  3. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Marcin Paprzycki

  4. Department of Computer Science and Engineering, National Institute of Technology, Delhi, India

    Narottam Chand Kaushal

  5. School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, Jiangsu, China

    Wei-Chiang Hong

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Pires, I.M. et al. (2020). A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices. In: Singh, P., Bhargava, B., Paprzycki, M., Kaushal, N., Hong, WC. (eds) Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's. Advances in Intelligent Systems and Computing, vol 1132. Springer, Cham. https://doi.org/10.1007/978-3-030-40305-8_33

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