All Outputs (4)

Designing Apps to Track Mental Workload (2023)
Presentation / Conference
Wilson, M., Shaban, J., Ma, X., Shalliker, M., Midha, S., & Sharples, S. (2023, September). Designing Apps to Track Mental Workload. Paper presented at The Future of Cognitive Personal Informatics, Athens, Greece and online

Brain-related wearables are now freely available on the market, and with even wrist-worn devices making estimates about cognitive activity, understanding Cognitive Personal Informatics (CogPI) has become a pressing issue. In this paper, we present a... Read More about Designing Apps to Track Mental Workload.

When High Mental Workload is Good and Low Mental Workload is Bad (2023)
Presentation / Conference
Shaban, J., Roy, M., Stephens-Marsh, M., Wilson, M. L., & Sharples, S. (2023, September). When High Mental Workload is Good and Low Mental Workload is Bad. Paper presented at The Future of Cognitive Personal Informatics, Athens, Greece and online

Brain-related wearables are now freely available on the market, and with even wrist-worn devices making estimates about cognitive activity, understanding cognitive personal informatics has become a pressing issue. Mental Workload is an emotionally ag... Read More about When High Mental Workload is Good and Low Mental Workload is Bad.

Designing for Reflection on our Daily Mental Workload (2023)
Conference Proceeding
Shaban, J., Wilson, M. L., & Sharples, S. (2023). Designing for Reflection on our Daily Mental Workload.

This paper presents a research plan, at the outset of new doctoral research, designing for reflection on cognitive personal informatics and self-tracking of Mental Workload. The research will build upon the Mental Workload cycle, considering how peop... Read More about Designing for Reflection on our Daily Mental Workload.

Resolving conflicts during human-robot co-manipulation (2023)
Conference Proceeding
Al-Saadi, Z., Hamad, Y. M., Aydin, Y., Kucukyilmaz, A., & Basdogan, C. (2023). Resolving conflicts during human-robot co-manipulation. In HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (243-251). https://doi.org/10.1145/3568162.3576969

This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish b... Read More about Resolving conflicts during human-robot co-manipulation.