A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies

Ch. Anwar Ul Hassan, Faten Khalid Karim (), Assad Abbas, Jawaid Iqbal, Hela Elmannai, Saddam Hussain, Syed Sajid Ullah () and Muhammad Sufyan Khan
Additional contact information
Ch. Anwar Ul Hassan: Department of Creative Technologies, Air University, Islamabad 44000, Pakistan
Faten Khalid Karim: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Assad Abbas: Department of Computer Science, COMSATS University, Islamabad 44000, Pakistan
Jawaid Iqbal: Faculty of Computing, Riphah International University, Islamabad 45210, Pakistan
Hela Elmannai: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Saddam Hussain: School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
Syed Sajid Ullah: Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
Muhammad Sufyan Khan: Department of Software Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan

Sustainability, 2023, vol. 15, issue 5, 1-20

Abstract: Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection.

Keywords: fall detections; fall-detection; 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
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