Visual Capability Estimation Using Motor Action Pattern

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 781)


Visual channel is a very import input for human recognition during complex tasks. The critical challenge is to tell exactly how well the visual capability of a subjects in real time dynamically. In this research, we assume that the visual capability of a subject varies according to the real task situation, and that the performance of the subject on the task is a very important measurement for estimation of the visual capability of the subject. We use the motor action pattern as the indicator to investigate the level of visual capability of the subjects in relation to the characteristics of the visual information, the nature of the task and state of the subject in a simulated task environment. The research found there was a strong indication that variation of the visual capability was related to the nature of the task and the state of the subject. The motor action pattern was good indicator for visual capability.


Visual capability Motor action pattern Recognition state 


  1. 1.
    Wang, Z., Fu, S.: A layered multi-dimensional description of pilot’s workload based on objective measures. In: International Conference on Engineering Psychology and Cognitive Ergonomics, pp. 203–211. Springer, Heidelberg (2013)Google Scholar
  2. 2.
    Farmer, E., Brownson, A.: QinetiQ: Review of Workload Measurement, Analysis and Interpretation Methods. European organization for the safety of air navigation (2003)Google Scholar
  3. 3.
    Smart Eye, A.B.: Smart-Eye Pro 5.6 User manual. Sweden-, Gothenburg, Smart Eye AB, Sweden Smart Eye AB (2009)Google Scholar
  4. 4.
    Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviat. Psychol. 12(1), 3–18 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Van Orden, K.F., Limbert, W., Makeig, S., Jung, T.P.: Eye activity correlates of workload during a visuospatial memory task. Hum. Factors 43(1), 111–121 (2001)CrossRefGoogle Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  7. 7.
    Tuovinen, J.E., Paas, F.: Exploring multidimensional approaches to the efficiency of instructional conditions. Instr. Sci. 32, 133–152 (2004)CrossRefGoogle Scholar
  8. 8.
    Rasmussen, C.E.: Gaussian processes in machine learning. In: Advanced Lectures on Machine Learning, pp. 63–71. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Aeronutics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina

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