Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

Hatem A. Alharbi, Khulud K. Alharbi () and Ch Anwar Ul Hassan ()
Additional contact information
Hatem A. Alharbi: Department of Computer Engineering, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia
Khulud K. Alharbi: Department of Health Services Management, College of Public Health and Health Informatics, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Ch Anwar Ul Hassan: Department of Creative Technologies, Air University, Islamabad 44000, Pakistan

Sustainability, 2023, vol. 15, issue 22, 1-27

Abstract: In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

Keywords: fall detections; RFID tags; IoT; artificial intelligence; cost-efficiency; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/22/15695/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/22/15695/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:22:p:15695-:d:1275582

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().