Enhancing Detection of Falls and Bed-Falls Using a Depth Sensor and Convolutional Neural Network

This study proposes an innovative fall detection system that leverages the capabilities of depth sensors and a convolutional neural network (CNN) model. The objective is to enhance fall detection accuracy by using raw data from depth images for ground reference establishment and to distinguish foreg...

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Bibliographic Details
Published in:IEEE sensors journal 2024-07, Vol.24 (14), p.23150-23162
Main Authors: Su, Mu-Chun, Chen, Jieh-Haur, Hsieh, Yi-Zeng, Hsu, Shu-Chien, Liao, Chia-Wei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Convolutional neural network (CNN)
Convolutional neural networks
depth sensor
elderly care
Fall detection
fall detection system
Feature extraction
Image color analysis
Image enhancement
Machine learning
Multilayer perceptrons
Radial basis function
Sensor systems
Sensors
Support vector machines
System reliability
video surveillance
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Summary:This study proposes an innovative fall detection system that leverages the capabilities of depth sensors and a convolutional neural network (CNN) model. The objective is to enhance fall detection accuracy by using raw data from depth images for ground reference establishment and to distinguish foreground elements by using a background subtraction algorithm for comprehensive analysis. The performance of the proposed CNN-based system was compared with that of systems based on three learning methods: support vector machine (SVM), multilayer perceptron (MLP), and radial basis function (RBF) neural network models. Experimental results indicated that the proposed system achieved an accuracy rate of approximately 95% and a Kappa coefficient of 0.96 in fall detection, thereby outperforming the other systems. These findings indicate that the proposed system has high efficacy and reliability; thus, it has potential applications in real-world scenarios requiring accurate fall detection.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3404031