TY - GEN
T1 - Subjective Workload Assessment Technique (SWAT) in Real Time
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
AU - Zak, Yuval
AU - Parmet, Yisrael
AU - Oron-Gilad, Tal
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Real-time continuous workload assessment is important for researchers and developers of tools that aim to reduce human operators' cognitive workload, especially in dynamic environments, as the military environment, where task demands and workload change rapidly. Most workload measurement techniques provide a single retrospective value or require expensive high-end sensing equipment. This study aimed to introduce an affordable continuous machine learning (ML) based workload assessment tool, that can provide real-time workload scores. Using experienced military unmanned aerial vehicle (UAV) operators in a simulated operational setting, muscle behavior represented by their interaction with a joystick was modeled to predict Subjective Workload Assessment Technique (SWAT) scores. Data were obtained from six professional participants. Four machine learning (ML) modeling methodologies were tested on each participant's data. It has been shown that after running an ML setup phase for each participant, an already in use available tool as the UAV joystick controller can be used to predict SWAT scores at any given time. By implementing the approach presented in this study, researchers can more accurately evaluate various aspects of the human operator's cognitive workload, and developers can evaluate the progression of their solutions on operators' cognitive workload over time.
AB - Real-time continuous workload assessment is important for researchers and developers of tools that aim to reduce human operators' cognitive workload, especially in dynamic environments, as the military environment, where task demands and workload change rapidly. Most workload measurement techniques provide a single retrospective value or require expensive high-end sensing equipment. This study aimed to introduce an affordable continuous machine learning (ML) based workload assessment tool, that can provide real-time workload scores. Using experienced military unmanned aerial vehicle (UAV) operators in a simulated operational setting, muscle behavior represented by their interaction with a joystick was modeled to predict Subjective Workload Assessment Technique (SWAT) scores. Data were obtained from six professional participants. Four machine learning (ML) modeling methodologies were tested on each participant's data. It has been shown that after running an ML setup phase for each participant, an already in use available tool as the UAV joystick controller can be used to predict SWAT scores at any given time. By implementing the approach presented in this study, researchers can more accurately evaluate various aspects of the human operator's cognitive workload, and developers can evaluate the progression of their solutions on operators' cognitive workload over time.
KW - cognitive workload
KW - machine learning
KW - real-time
KW - subjective workload assessment technique
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85098858089&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9283168
DO - 10.1109/SMC42975.2020.9283168
M3 - Conference contribution
AN - SCOPUS:85098858089
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2687
EP - 2694
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers
Y2 - 11 October 2020 through 14 October 2020
ER -