A novel computer vision based data driven modelling approach for person specific fall detection

Gong, Liyun, Yu, Miao, Zhu, Ming , Clifford, Ross, Duff, Carol, Ye, Xujiong and Kollias, Stefanos (2021) A novel computer vision based data driven modelling approach for person specific fall detection. Journal of Ambient Intelligence and Smart Environments, 13 (5). ISSN 1876-1364

Full content URL: https://doi.org/10.3233/AIS-210611

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A novel computer vision based data driven modelling approach for person specific fall detection
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Abstract

In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person's normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.

Keywords:healthcare, fall detection, data driven model, convolutional neural network, convolutional neural network autoencoder, one class support vector machine
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:46526
Deposited On:25 Oct 2021 07:57

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