Video-based infant monitoring using a CNN-LSTM scheme

Lan Min; Yue Sun; Peter H. N. de With

doi:10.1117/12.2581026

15 February 2021 Video-based infant monitoring using a CNN-LSTM scheme

Lan Min, Yue Sun, Peter H. N. de With

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159717 (2021) https://doi.org/10.1117/12.2581026
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Infants are sensitive to pain and discomfort in their daily lives and frequent discomfort and pain can even lead to abnormal brain development, resulting in long-term adverse neurodevelopmental outcomes. With the help of video-based monitoring, a contactless method is considered to be promising for detecting discomfort automatically. In this study, a method for distinguishing infant discomfort status from comfort is proposed. We first extract two-dimensional (2D) features from video frames using pretrained Convolutional Neural Networks (CNNs), which is followed by two different Long Short-term Memory (LSTM) networks (uni- and bi-directional LSTMs) for the comfort/discomfort classification task. The methods are evaluated using videos acquired from 23 infants. Experimental results show the best AUC of 0.89 is achieved when using the bi-directional LSTM model based on the 2D features extracted by the VGG16 network. The high detection score indicates that the proposed method is promising for clinical use.

Conference Presentation

Citation Download Citation

Lan Min, Yue Sun, and Peter H. N. de With "Video-based infant monitoring using a CNN-LSTM scheme", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159717 (15 February 2021); https://doi.org/10.1117/12.2581026

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