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Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Author

Listed:
  • Grigorios Kyriakopoulos

    (School of Electrical and Computer Engineering, Electric Power Division, Photometry Laboratory, National Technical University of Athens, 9 Heroon Polytechniou Street, 15780 Athens, Greece)

  • Stamatios Ntanos

    (Department of Business Administration, University of West Attica, Thivon 250, Egaleo, 122 44 Athens, Greece)

  • Theodoros Anagnostopoulos

    (Department of Business Administration, University of West Attica, Thivon 250, Egaleo, 122 44 Athens, Greece
    Department of Infocommunication Technologies, ITMO University, Kronverksiy Prospekt, 49, St. Petersburg 197101, Russia)

  • Nikolaos Tsotsolas

    (Department of Business Administration, University of West Attica, Thivon 250, Egaleo, 122 44 Athens, Greece)

  • Ioannis Salmon

    (Department of Business Administration, University of West Attica, Thivon 250, Egaleo, 122 44 Athens, Greece)

  • Klimis Ntalianis

    (Department of Business Administration, University of West Attica, Thivon 250, Egaleo, 122 44 Athens, Greece)

Abstract

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

Suggested Citation

  • Grigorios Kyriakopoulos & Stamatios Ntanos & Theodoros Anagnostopoulos & Nikolaos Tsotsolas & Ioannis Salmon & Klimis Ntalianis, 2020. "Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:408-:d:306320
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    References listed on IDEAS

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    1. Shabir Ahmad & Sehrish Malik & Israr Ullah & Dong-Hwan Park & Kwangsoo Kim & DoHyeun Kim, 2019. "Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
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    Cited by:

    1. Yu Shao & Xinyue Wang & Wenjie Song & Sobia Ilyas & Haibo Guo & Wen-Shao Chang, 2020. "Feasibility of Using Floor Vibration to Detect Human Falls," IJERPH, MDPI, vol. 18(1), pages 1-22, December.

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