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

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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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Notes

  1. Similar pattern of results were observed in case of two-fold cross validation. We did not show these results due to space constraint.

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Authors and Affiliations

  1. Faculty of Telecom and Information Engineering, University of Engineering and Technology, Taxila, Pakistan

    Rida Ghafoor Hussain, Mustansar Ali Ghazanfar & Muhammad Awais Azam

  2. School of Architecture, Computing and Engineering, University of East London, Docklands Campus, London, UK

    Usman Naeem & Shafiq Ur Rehman

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  1. Rida Ghafoor Hussain
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  2. Mustansar Ali Ghazanfar
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  3. Muhammad Awais Azam
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  4. Usman Naeem
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  5. Shafiq Ur Rehman
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Correspondence to Rida Ghafoor Hussain.

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Hussain, R.G., Ghazanfar, M.A., Azam, M.A. et al. A performance comparison of machine learning classification approaches for robust activity of daily living recognition. Artif Intell Rev 52, 357–379 (2019). https://doi.org/10.1007/s10462-018-9623-5

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