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A novel fall detection algorithm for elderly using SHIMMER wearable sensors

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Abstract

Fall is one of the major cause of deaths in elderly along with other chronic diseases in all over the world. Therefore, it is important to find a cost effective, non-intrusive and lightweight solution for early fall detection and prevention in elderly. Several fall detection systems have been proposed, using the different types of sensors and techniques. In this paper, a novel fall detection technique, using the wearable SHIMMER™ sensors, is proposed, which identifies the fall event, using Mahalanobis distance on real-time data. It is more robust than other conventional distance measure techniques, followed in existing fall detection systems. We first developed a real dataset that consists of three daily life activities, such as walking, sitting (on) and getting up (from) a chair, and standing still. These activities are the main cause of fall in elderlies. The proposed algorithm was tested and validated, to identify the fall event. It produced the promising results, which are comparable to the state-of-the-art fall detection techniques.

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Acknowledgements

This research is partially supported by ICT R&D project in Pakistan and Deanship of Research Grant, Islamic University of Madinah, Saudi Arabia. The authors wish to acknowledge the assistance of Mr. Kashif Rizwan, Faraz, Mutahhir, Faheem and Moiz Ahmed for data collection as part of their undergraduate project at Federal Urdu University and University of Pakistan. We also acknowledge the administration of Dar-ul-Sukoon (old age home, Pakistan) and Edhi foundation old age homes at two locations in Karachi, Pakistan.

Funding

Part of this research is supported by the Federal Urdu University Pakistan and Islamic University Madinah through deanship of research project.

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Authors and Affiliations

  1. Federal Urdu University of Arts, Science & Technology, Karachi, Pakistan

    Amir Mehmood, Adnan Nadeem & Muhammad Ashraf

  2. NED University of Engineering & Technology, Karachi, Pakistan

    Amir Mehmood

  3. Islamic University of Madinah, Madinah, Kingdom of Saudi Arabia

    Adnan Nadeem, Turki Alghamdi & Muhammad Shoaib Siddiqui

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  1. Amir Mehmood
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  2. Adnan Nadeem
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  3. Muhammad Ashraf
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  4. Turki Alghamdi
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  5. Muhammad Shoaib Siddiqui
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Correspondence to Amir Mehmood.

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Conflict of interest

Authors have no conflict of interest with any individual or organization regarding the study in this paper.

Ethical standard

The study involves data collection of certain daily life activities through wearable shimmer sensor on normal humans. All procedures followed were in accordance with the ethical standards of the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.”

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The consent form was signed with the organizations and individual before the experiments. The process also ensures the privacy of the identity of the subjects.

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Mehmood, A., Nadeem, A., Ashraf, M. et al. A novel fall detection algorithm for elderly using SHIMMER wearable sensors. Health Technol. 9, 631–646 (2019). https://doi.org/10.1007/s12553-019-00298-4

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  • DOI: https://doi.org/10.1007/s12553-019-00298-4

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