Capturing spatio-temporal patterns of falls individuals using efficient graph convolutional network model Capturing spatio-temporal patterns of falls individuals using efficient graph convolutional network model

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 55; no. 11; p. 825
• Main Authors: Guendoul, Oumaima, Zobi, Maryem, Ait Abdelali, Hamd, Tabii, Youness, Oulad Haj Thami, Rachid, Bourja, Omar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Comparative studies; Computer Science; Datasets; Fall detection; Human motion; Machines; Manufacturing; Mechanical Engineering; Neural networks; Older people; Processes; Sensors; Support vector machines; Skeleton-based; Spatial temporal network; Fall detection; Graph convolution network; Kinect data
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-025-06316-5

Falls are a major worldwide health concern among people, and the ability to detect and prevent falls can have significant implications for their safety and well-being. This paper uses an Efficient-Graph Convolutional Network (Efficient-GCN) model to extract discriminative features of fall actions. The proposed model is designed to handle the complex and dynamic nature of human movements during a fall event. The main problem in fall events is to capture spatiotemporal information that results from falls, plus the insufficient data size for training. To address this problem, we suggest a protocol to collect a fall dataset. The Kinect camera is used to collect skeleton data, which is then processed using the Efficient-Graph Convolutional Network (Efficient-GCN) algorithm to identify fall individual patterns. We present a comparative study between three methods Efficient-Graph Convolutional Network (Efficient-GCN), Support Vector machine (SVM), and k-nearest neighbor (KNN) for improving skeletal-based fall detection and deep convolutional neural network (DCNN) for depth data. To have a more global view we compare our results with public dataset on the three baselines variant noted as Baseline coefficient (Bx) where “x” denotes scaling coefficient, where Efficient-Graph Convolutional Network Baseline with coefficient 2 (Efficient-GCN-B2) on our collected dataset outperforms achieving 98,50% accuracy on the cross-subject. The Efficient-Graph Convolutional Network with coefficient 2 (Efficient-GCN-B2) algorithm achieves remarkably satisfactory results in detecting fall events on the robust representation which is a skeleton and Deep Convolutional Neural Network (DCNN) attains 97% on depth data.