A Detection Method of Temporary Rest State While Performing Mental Works by Measuring Physiological Indices
In order to evaluate intellectual productivity such as the efficiency of performing mental works, several studies were conducted where specially designed tasks were given. However, the result may not be reflected the actual intellectual productivity because the designed tasks are different from office works. Meanwhile, there are two mental states (work and temporary rest state) in office workers which are changing alternatively during mental work and the ratio of the two states reflects the productivity. If the mental states of the workers can be detected, the productivity can be measured more accurately. In this study, a detection method of temporary rest state while performing mental works by measuring physiological indices has been developed. As the result of the subject experiment, it was found that the detection accuracy was 80.2%. This result shows the possibility to use the physiological indices as one of the mental state detection methods.
Keywordsintellectual productivity physiological psychology cognitive psychology office work mental work
Unable to display preview. Download preview PDF.
- 1.Obayashi, F., Tomita, K., Hattori, Y., Kawauchi, M., Shimoda, H., Ishii, H., Terano, M., Yoshikawa, H.: A Study on Environmental Control Method to Improve Productivity of Office Workers Development of an Illumination Control Method and its Experimental Evaluation. Human Interface Society 1, 151–156 (2006)Google Scholar
- 2.Miyagi, K., Kawano, S., Ishii, H., Shimoda, H.: Improvement and Evaluation of Intellectual Productivity Model Based on Work State Transition. In: The 2012 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1491–1496 (2012)Google Scholar
- 3.Miyagi, K., Kondo, Y., Enomoto, K., Ishii, H., Shimoda, H., Iwakawa, M., Terano, M.: Measurement of Brain Activity with Near-Infrared Spectroscopy during Performance Test for Assessing Improvement of Intellectual Productivity. Human Interface 10, 149–154 (2008)Google Scholar
- 4.Hosseini, S.A., Khalilzadeh, M.A.: Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State. In: 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS), pp. 1–6 (2010)Google Scholar
- 5.Omi, N., Morimoto, Y., Yokoyama, K., Mizuno, Y., Takata, K.: Heart Rate Variability Analysis during Long Distance Driving Using Wavelet Transform. Technical Report of IEICE 99, 9–14 (1999)Google Scholar
- 6.Omi, N., Morimoto, Y., Yokoyama, K., Mizuno, Y., Takata, K.: Application of Wavelet Analysis to Heart Rate Variability. Technical Report of IEICE 97, 47–52 (1998)Google Scholar
- 9.Fukuda, K., Stern, J.A., Brown, T.R., Russo, M.B.: Cognition, Blinks, Eye-Movements, and Pupillary Movements During Performance of a Running Memory Task. Aviation, Space, and Environmental Medicine 76, C75–C85 (2005)Google Scholar
- 10.Bursteinm, K.R., Fenz, W.D., Bergeron, J., Epstei, S.: A comparison of skin potential and skin resistance responses as measures of emotional responsivity. Psychophysiology 2, 12–24 (1965)Google Scholar
- 11.Umezawa, A., Kurohara, A.: A Comparison of Skin Conductance and Skin Potential as an Index in Electrodermal Biofeedback Studies. Japanese Society of Biofeedback Research 21, 26–36 (1994)Google Scholar