Abstract
The neuropsychiatric disorders have become a high risk among the elderly group and their group of patients has the tendency of getting younger. However, an efficient computer-aided system with the computer vision technique to detect the neuropsychiatric disorders has not been developed yet. More specifically, there are two critical issues: (1) the postures between various neuropsychiatric disorders are similar, (2) lack of physiotherapists and expensive examinations. In this study, we design an innovative framework which associates a novel two-dimensional feature map with a convolutional neural network to identify the neuropsychiatric disorders. Firstly, we define the seven types of postures to generate the one-dimensional feature vectors (1D-FVs) which can efficiently describe the characteristics of neuropsychiatric disorders. To further consider the relationship between different features, we reshape the features from one-dimensional into two-dimensional to form the feature maps (2D-FMs) based on the periods of pace. Finally, we generate the identification model by associating the 2D-FMs with a convolutional neural network. To evaluate our work, we introduce a new dataset called Simulated Neuropsychiatric Disorders Dataset (SNDD) which contains three kinds of neuropsychiatric disorders and one healthy with 128 videos. In experiments, we evaluate the performance of 1D-FVs with classic classifiers and compare the performance with the gait anomaly feature vectors. In addition, extensive experiments conducting on the proposed novel framework which associates the 2D-FMs with a convolutional neural network is applied to identify the neuropsychiatric disorders.
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Lin, CW., Ding, Q. (2019). Neuropsychiatric Disorders Identification Using Convolutional Neural Network. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_26
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DOI: https://doi.org/10.1007/978-3-030-05716-9_26
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