Neuropsychiatric Disorders Identification Using Convolutional Neural Network

  • Chih-Wei Lin
  • Qilu Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)


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.


Neuropsychiatric disorder Posture motion Symptoms Depth sensors Convolutional neural network 


  1. 1.
    Auvinet, E., Multon, F., Manning, V., Meunier, J., Cobb, J.: Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using microsoft kinect data. Gait Posture 51, 162–168 (2017)CrossRefGoogle Scholar
  2. 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
  3. 3.
    Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., Rochester, L.: Accuracy of the microsoft kinect sensor for measuring movement in people with parkinson’s disease. Gait Posture 39(4), 1062–1068 (2014)CrossRefGoogle Scholar
  4. 4.
    Galna, B., et al.: Retraining function in people with parkinsons disease using the microsoft kinect: game design and pilot testing. J. Neuroeng. Rehabi. 11(1), 60 (2014)CrossRefGoogle Scholar
  5. 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
  6. 6.
    González-Ortega, D., Díaz-Pernas, F., Martínez-Zarzuela, M., Antón-Rodríguez, M.: A kinect-based system for cognitive rehabilitation exercises monitoring. Comput. Methods Programs Biomed. 113(2), 620–631 (2014)CrossRefGoogle Scholar
  7. 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). Scholar
  8. 8.
    Higashiguchi, T., Shimoyama, T., Ukita, N., Kanbara, M., Hagita, N.: Classification of gait anomaly due to lesion using full-body gait motions. IEICE Trans. Inf. Syst. 100(4), 874–881 (2017)CrossRefGoogle Scholar
  9. 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
  10. 10.
    Pompeu, J., et al.: Feasibility, safety and outcomes of playing kinect adventures! for people with parkinson’s disease: a pilot study. Physiotherapy 100(2), 162–168 (2014)CrossRefGoogle Scholar
  11. 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
  12. 12.
    Schmitz, A., Ye, M., Shapiro, R., Yang, R., Noehren, B.: Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system. J. Biomech. 47(2), 587–591 (2014)CrossRefGoogle Scholar
  13. 13.
    Snijders, A.H., Van De Warrenburg, B.P., Giladi, N., Bloem, B.R.: Neurological gait disorders in elderly people: clinical approach and classification. Lancet Neurol. 6(1), 63–74 (2007)CrossRefGoogle Scholar
  14. 14.
    Stolze, H., et al.: Typical features of cerebellar ataxic gait. J. Neurol. Neurosurg. Psychiatry 73(3), 310–312 (2002)CrossRefGoogle Scholar
  15. 15.
    Yeung, L., Cheng, K.C., Fong, C., Lee, W.C., Tong, K.Y.: Evaluation of the microsoft kinect as a clinical assessment tool of body sway. Gait Posture 40(4), 532–538 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer and Information ScienceFujian Agriculture and Forestry UniversityFuzhouChina

Personalised recommendations