article

A performance comparison of machine learning classification approaches for robust activity of daily living recognition

Authors:

Rida Ghafoor Hussain,

Mustansar Ali Ghazanfar,

Muhammad Awais Azam,

Shafiq Ur RehmanAuthors Info & Claims

Artificial Intelligence Review, Volume 52, Issue 1

Pages 357 - 379

https://doi.org/10.1007/s10462-018-9623-5

Published: 06 August 2019 Publication History

Abstract

We live in a world surrounded by ubiquitous devices that capture data related to our daily activities. Being able to infer this data not only helps to recognise activities of daily life but can also allow the possibility to recognise any behavioural changes of the person being observed. This paper presents a performance comparison of a series of machine learning classification techniques for activity recognition. An existing hierarchal activity recognition framework has been adapted in order to assess the performance of five machine learning classification techniques. We performed extensive experiments and found that classification approaches significantly outperform traditional activity recognition approaches. The motivation of the work is to enable independent living among the elderly community, namely patients suffering from Alzheimer's disease.

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Cited By

Khan UMaqsood MGillani SDurrani MMehmood ISeo S(2022)A deep learning-based framework for accurate identification and crop estimation of olive treesThe Journal of Supercomputing10.1007/s11227-022-04738-379:2(1834-1855)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1007/s11227-022-04738-3

Index Terms

A performance comparison of machine learning classification approaches for robust activity of daily living recognition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

Index terms have been assigned to the content through auto-classification.

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 52, Issue 1

June 2019

721 pages

ISSN:0269-2821

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Copyright © Copyright © 2019 Springer Nature B.V.

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Kluwer Academic Publishers

United States

Publication History

Published: 06 August 2019

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Cited By

Khan UMaqsood MGillani SDurrani MMehmood ISeo S(2022)A deep learning-based framework for accurate identification and crop estimation of olive treesThe Journal of Supercomputing10.1007/s11227-022-04738-379:2(1834-1855)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1007/s11227-022-04738-3

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