Neuropsychiatric Disorders Identification Using Convolutional Neural Network
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.
KeywordsNeuropsychiatric disorder Posture motion Symptoms Depth sensors Convolutional neural network
- 2.Cunha, J.P.S., et al.: A novel portable, low-cost kinect-based system for motion analysis in neurological diseases. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 2339–2342. IEEE (2016)Google Scholar
- 5.Gehlsen, G., Beekman, K., Assmann, N., Winant, D., Seidle, M., Carter, A.: Gait characteristics in multiple sclerosis: progressive changes and effects of exercise on parameters. Arch. Phys. Med. Rehabil. 67(8), 536–539 (1986)Google Scholar
- 7.Higashiguchi, T., Shimoyama, T., Ukita, N., Kanbara, M., Hagita, N.: Lesioned-part identification by classifying entire-body gait motions. In: Bräunl, T., McCane, B., Rivera, M., Yu, X. (eds.) PSIVT 2015. LNCS, vol. 9431, pp. 136–147. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29451-3_12CrossRefGoogle Scholar
- 9.Ortiz-Gutiérrez, R., Cano-de-la Cuerda, R., Galán-del Río, F., Alguacil-Diego, I.M., Palacios-Ceña, D., Miangolarra-Page, J.C.: A telerehabilitation program improves postural control in multiple sclerosis patients: a spanish preliminary study. Int. J. Environ. Res. Publ. Health 10(11), 5697–5710 (2013)CrossRefGoogle Scholar
- 11.Procházka, A., Schätz, M., Ťupa, O., Yadollahi, M., Vysata, O., Walls, M.: The MS kinect image and depth sensors use for gait features detection. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2271–2274. IEEE (2014)Google Scholar