Human factors assessment

We use experimentation, simulation, and heuristic evaluation methods to identify human factors issues and assess the effectiveness of solutions.

Customized assessment plans are created for each project to comprehensively examine all important factors and conditions. Measuring variables such as human performance, workload, and situation awareness provides the empirical data for the assessment of human factors in complex human-machine interaction. Our projects have covered a wide range of application domains including driving, virtual and augmented reality, human-automation interaction, and in door environment optimization.

indoor navigation

Related publications, theses, and grants

Virtual and augmented reality

  • Cao, S., Nandakumar, K., Babu, R., & Thompson, B. (2020). Game play in virtual reality driving simulation involving head-mounted display and comparison to desktop display. Virtual Reality [IF 2019: 2.941], 24(3), 503-513.
  • Rehman, U., & Cao, S. (2020). Comparative evaluation of augmented reality-based assistance for procedural tasks: A simulated control room study. Behaviour and Information Technology [IF 2019: 1.781], 39(11), 1225-1245.
  • Rehman, U., & Cao, S. (2017). Augmented reality-based indoor navigation: A comparative analysis of handheld devices vs. Google Glass. IEEE Transactions on Human-Machine Systems [IF 2019: 5.247], 47(1), 140-151.
  • Ho, A., Maritan, C., Sullivan, J., Cheng, E., & Cao, S. (2016). Measuring glance legibility of wearable heads-up display interfaces using an adaptive staircase procedure: A pilot study with Google Glass. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60, 2073-2077.

Human-automation interaction

  • Dikmen, M., Li, Y., Ho, G., Farrell, P., Cao, S., & Burns, C. (2020). The burden of communication: Effects of automation support and automation transparency on team performance. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2227-2231).
  • Deng, C., Cao, S., Wu, C., & Lyu, N. (2019). Modeling driver take-over reaction time and emergency response time using an integrated cognitive architecture. Transportation Research Record [IF 2019: 1.029], 2673(12), 380-390.
  • Dikmen, M., Li, Y., Farrell, P., Ho, G., Cao, S., & Burns, C. (2019). The effects of automation and role allocation on team performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63. 235-239.

Driving

  • Cao, S., Tang, P., & Sun, X. (2020). Driver take-over reaction in autonomous vehicles with rotatable seats. Safety, 6(3), 34.
  • Rehman, U., Cao, S., & MacGregor, C. (2019). Using an integrated cognitive architecture to model the effect of environmental complexity on drivers’ situation awareness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63. 812-816.
  • Natural Sciences and Engineering Research Council (NSERC) Engage Grant. Human data collection protocol for image-based driver monitoring system validation.
driving assessment


In door environment

  • Sun, B., Cao, S., & Li, Z. (2018). The impact of classroom lighting on student performance: A literature review. Chinese Journal of Applied Psychology, 24(4), 291-303.
  • Natural Sciences and Engineering Research Council (NSERC) Engage Grant. Human factors and ergonomics improvements in a Cambridge manufacturing company