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

Grigorios Kyriakopoulos, Stamatios Ntanos, Theodoros Anagnostopoulos, Nikolaos Tsotsolas, Ioannis Salmon and Klimis Ntalianis
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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
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

IJERPH, 2020, vol. 17, issue 2, 1-14

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.

Keywords: elderly and impaired; healthcare; Internet of Things (IoT); fall verification; temporal inference model; smart homes (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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