TY - GEN
T1 - Prediction of temperature induced office worker's performance during typing task using EEG
AU - Nayak, Tapsya
AU - Zhang, Tinghe
AU - Mao, Zijing
AU - Xu, Xiaojing
AU - Pack, Daniel J.
AU - Dong, Bing
AU - Huang, Yufei
N1 - Funding Information:
*Research partly supported by Army Research Laboratory under Cooperative Agreement Number W911NF-10-2-0022. 1. Department of Electrical & Computer Engineering, 2. Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249 USA 3. NSF-DOE CURRENT, University of Tennessee, Knoxville, TN 37996-2250, USA 4. Department of Engineering and Computer Science at University of Tennessee at Chattanooga, Chattanooga, Tennessee, 37403, USA Contact: Yufei Huang (e-mail: Yufei.Huang@utsa.edu) and Bing Dong
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Understanding how indoor environment affects office worker's performance and developing methods to predict human performance in changing indoor environment have become highly important research topic bearing significant economic and sociological impact. While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram (EEG), during simulated office-work tasks under different indoor room temperatures (22°C and 30°C). We found that using brain power spectral densities (PSD) from EEG as predictors provides the higher R2 than predictors using skin temperature or heart rate by approximately over 3 folds. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate. Our work shows the potential of using brain signals to accurately predict human office work performance.
AB - Understanding how indoor environment affects office worker's performance and developing methods to predict human performance in changing indoor environment have become highly important research topic bearing significant economic and sociological impact. While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram (EEG), during simulated office-work tasks under different indoor room temperatures (22°C and 30°C). We found that using brain power spectral densities (PSD) from EEG as predictors provides the higher R2 than predictors using skin temperature or heart rate by approximately over 3 folds. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate. Our work shows the potential of using brain signals to accurately predict human office work performance.
KW - Electroencephalography (EEG)
KW - Human performance
KW - Indoor room temperature
KW - Office-work tasks
KW - Performance prediction
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U2 - 10.1109/EMBC.2017.8037165
DO - 10.1109/EMBC.2017.8037165
M3 - Conference contribution
C2 - 29060209
AN - SCOPUS:85032211803
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1684
EP - 1687
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -