Journal of Robotics Spectrum

Mental State Adaptive Interfaces as a Remedy to the Issue of Long-term, Continuous Human Machine Interaction

Journal of Robotics Spectrum

Received On : 25 January 2023

Revised On : 28 February 2023

Accepted On : 05 March 2023

Published On : 16 March 2023

Volume 01, 2023

Pages : 078-089


In order to promote safer and more efficient human-machine interaction, this article advocates for the employment of adaptive systems that account for the user's mental state throughout the duration of lengthy, continuous usage. Perhaps what is needed are adaptive systems that can adjust to the user's mood. The operator's state of mind may be inferred using a combination of operator-independent metrics (for instance, time of day and weather) and behavior (for instance, lane deviation and response time) and physiological (for instance, heart activity and electroencephalography) indicators. Several changes may be made to the dynamic between the operator and the system to mitigate the impacts of the operator's diminished cognitive capacity and preserve the reliability and efficacy of operations. Depending on the specifics of the job at hand and the difficulties that must be overcome, adjustments may be made to factors such as the type of the information presented, the structure of the presentation, the prominence of the stimuli, and the order in which the tasks are performed, frequently using the predictions produced by machine learning.


Human-Machine Interaction, Mental State Adaptive Interfaces, Mental Fatigue, Cognitive Flexibility, Brain-Computer Interfaces, Situational Awareness.

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Author(s) thanks to University of Nairobi for research lab and equipment support.


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John Huria Nderitu, “AMental State Adaptive Interfaces as a Remedy to the Issue of Long-term, Continuous Human Machine Interaction”, Journal of Robotics Spectrum, vol.1, pp. 078-089, 2023. doi: 10.53759/9852/JRS202301008.


© 2023 John Huria Nderitu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.