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Open AccessArticle

Posture Recognition Using Ensemble Deep Models under Various Home Environments

1
Department of Control and Instrumentation Engineering, Chosun University, Gwangju 61452, Korea
2
Intelligent Robotics Research Division, Electronics Telecommunications Research Institute, Daejeon 61452, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1287; https://doi.org/10.3390/app10041287
Received: 26 December 2019 / Revised: 6 February 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
(This article belongs to the Section Computing and Artificial Intelligence)
This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself. View Full-Text
Keywords: ensemble deep models; convolutional neural network; posture recognition; preconfigured CNNs; posture database; home environments ensemble deep models; convolutional neural network; posture recognition; preconfigured CNNs; posture database; home environments
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MDPI and ACS Style

Byeon, Y.-H.; Lee, J.-Y.; Kim, D.-H.; Kwak, K.-C. Posture Recognition Using Ensemble Deep Models under Various Home Environments. Appl. Sci. 2020, 10, 1287.

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