International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 4, August 2023, pp. 4721∼4733 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4721-4733 ❒ 4721 EdgeFall: a promising cloud-edge-end architecture for elderly fall care Kazi Md Shahiduzzaman, Md. Salah Uddin Yusuf Department of Electrical and Electrical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh Article Info Article history: Received Jul 4, 2022 Revised Oct 26, 2022 Accepted Nov 6, 2022 Keywords: Accelerometer Elderly care End-edge-cloud architecture Gyroscope Human activity Long term short memory Wearable camera ABSTRACT Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipi- tous fall, carry out their communal life narrowed. Therefore, a shrewd and ad- equate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have sug- gested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities. This is an open access article under the CC BY-SA license. Corresponding Author: Md. Salah Uddin Yusuf Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology Khulna, Bangladesh Email: suyusuf@eee.kuet.ac.bd 1. INTRODUCTION Any abnormality in everyday human activity brings an unusual fall. An adequate routine human activity recognition can identify, anticipate and inhibit many diseases. Walking, running (jogging), jumping, walking stairs, sitting, standing, and laying are closely related to elderly fall-related service [1]–[3]. Besides, human activity recognition is an elemental part of most explorations concerned to fall detection and prediction [3]. That is why we choose to lead our investigation with human activity recognition from the context of fall detection and prediction. We can express the interpretation of fall as the sudden change of state from a higher post ion like standing to a lower position like laying or sitting [4]. People over 65 years of age are at significant risk of falling, corresponding to many published articles. A survey from the World Health Organization (WHO) shows that there are around 650 thousand injuries occur that are linked to falls every year [1], [5]–[7]. These falls induce curable or incurable injuries from flesh cramping, fractured extremities, posterior damage, and brain injury to life expiry. Every inmate has to put in an abundance of payment in medicine, therapy, and treatment [4], [8]–[10]. The after-effects of falls are likewise severe as they lead to falls, depression, restraint Journal homepage: http://ijece.iaescore.com