A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

Abstract

As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.

Perspectives on Artificial Intelligence Adoption for European Union Elderly in the Context of Digital Skills Development

“…In the context of analyzing the relationship between artificial intelligence and elderly individuals, a notable contribution emerged from the paper titled "A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing" [70]. The paper underlines the escalating global trend of aging populations and the consequential increase in falls among the elderly, which have become a prominent cause of unintentional fatalities.…”

“…Malta (56), Czech Republic (57), Spain (57), Greece (60), Portugal (63), Belgium (65), France (68), Poland (68), Bulgaria (70), Slovenia (71), Croatia (73), Romania (90), Slovakia (100) Previous research in the field suggests that in countries with lower power distance, there is a greater tendency to accept technology [92], while, according to Jayaprakash et al, countries with a lower power distance favor the efficient use of technologies, with communication being direct and participatory, which makes all members of society more easily accessible [93]. Although considering a different context, Pfaff, Yuko Melanie, et al findings highlight the fact that reducing the power distance is essential in digital transformation [94].…”

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

“…For instance, refs. [4][5][6][7][8] made significant achievements in energy efficiency through edge computing and optimizing mechanisms, resulting in a reduced network bandwidth and response time in IoT-based smart video surveillance systems for effective object detection and abnormal behavior analysis. Moreover, ref.…”

Development of artificial intelligence edge computing based wearable device for fall detection and prevention of elderly people

“…To meet the computational demands of deep learning models, the integration of edge computing devices gives a promising solution. With the help of edge computing, the wearable sensor-based fall detection can achieve enhanced efficiency and responsiveness [ [25] , [26] , [27] , [28] , [29] , [30] ]. These devices can pre-process sensor data, extract relevant features, and even execute lightweight versions of deep learning models locally in the wearable sensors or at a nearby server.…”