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IoT and Machine Learning Algorithms for Fall Detection

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Wearable/Personal Monitoring Devices Present to Future

Abstract

Context—Fall is a vital health risk for the elderly worldwide. Every year, approximately 37 million people need medical attention due to falls in which the mostly affected are the elderly. Fall-related injuries are found to be the leading cause for them to go to an emergency room in hospitals. It increases the dependency on caregivers and reduces the quality of life. So automatic and pre-detection of falls is necessary to resolve the issues. Methodology—The studies included for analysis in the chapter are from the period 2013–2020. Approximately, 1000 book chapters, research, and review articles are extracted using the relevant search strings. Among them, 25 studies from reputed journals related to IoT and machine learning algorithms are used here. Conclusion—The study finds the task of the Internet of Things (IoT) and machine learning in fall detection, and how IoT works as a mediator for sharing the situation of patients or the elderly with medical staff, ambulance, and caretakers. The study focused on the pros and cons of IoT for various fall detection algorithms. It also described the security threats in IoT devices to make the individual's health record safe from unauthorized people but to be able to share with medical staff. So the safety of IoT becomes a significant parameter. Significance—Wearable devices and sensors using machine learning tools play a significant role in fall detection. Cost and time can be reduced by connecting it with Internet of Things (IoT). IoT provides a comfortable environment for the elderly at home than in hospitals, where they can engage in meetings with medical staff happily.

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, DR BR Ambedkar National Institute of Technology, Jalan dhar, India

    Pooja, S. K. Pahuja & Karan Veer

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  1. Pooja
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  2. S. K. Pahuja
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  3. Karan Veer
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Correspondence to Karan Veer .

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Editors and Affiliations

  1. BioMedical Engineering and Instrumentation, Western Sydney University, Penrith, NSW, Australia

    Gaetano D. Gargiulo

  2. MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia

    Ganesh R. Naik

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Pooja, Pahuja, S.K., Veer, K. (2022). IoT and Machine Learning Algorithms for Fall Detection. In: Gargiulo, G.D., Naik, G.R. (eds) Wearable/Personal Monitoring Devices Present to Future. Springer, Singapore. https://doi.org/10.1007/978-981-16-5324-7_10

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  • DOI: https://doi.org/10.1007/978-981-16-5324-7_10

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