Vision-based Human Posture Classification and Fall Detection using Convolutional Neural Network

Hasib, Rabia, Khan, Kaleem, Yu, Miao and Khan, Muhammad (2021) Vision-based Human Posture Classification and Fall Detection using Convolutional Neural Network. In: 2021 International Conference on Artificial Intelligence (ICAI 2021).

Full content URL: https://doi.org/10.1109/ICAI52203.2021.9445263

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

In this paper, we propose a novel computer vision based system for detecting falls on the basis of a monocular camera that can be applied for assisting independent living of an older adult living alone at home. From the recorded video data, mask regional convolutional neural network (mask R-CNN) is first applied to detect and extract the human silhouettes. The low-dimensionality representative features of extracted silhouettes are then extracted by convolutional neural network (CNN), which is applied for classification of different human postures (e.g., sit, bend, stand and lie) and a detection of a fall event (i.e., lying posture detected on the ground). For performance comparison, several machine learning algorithms such as support vector machine (SVM), random forest (RF), k-means clustering and artificial neural network (ANN) are trained with the same dataset. The proposed framework proved to be stable in human posture detection and achieved an overall accuracy of 97.5% and F-measure of 97% with small false alarms. On the same dataset the accuracies of other competing models, i.e., ANN, SVM, RF, and k-means are; 96.56%, 86.95%, 91.92% and 40.06%, respectively.

Keywords:fall detection, Machine learning, convolutional neural network, Deep learning, computer vision
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:46504
Deposited On:21 Oct 2021 14:43

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