Exploration of Mandibular Inputs for Human-Machine Interfaces

Abdulaziz Yaslam, Eric Feron

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The direct connection of the jaw to the brain allows it to retain its motor and sensory capabilities even after severe spinal cord injuries. As such, it can be an accessible means of providing inputs for people with paralysis to manipulate their environment. This paper explores the potential for using the jaw, specifically the mandible, as an alternative input to human-machine interface systems. Two tests were developed to test the mandible’s ability to respond to visual stimuli. First, a visual response time test to measure the precision and accuracy of user input through a mandible-actuated button. Second, a choice response test to observe coordination between the mandible and a finger.Study results show that the mean response time of mandible inputs is 8.3% slower than the corresponding mean response time of performing the same task with a thumb. The delay in response after making a decision is statistically insignificant between the mandible-and finger-actuated inputs with the mandible being 2.67% slower.Based on these results, the increase in response time while using the mandibular input is minimal for new users. Coordination is feasible in tasks involving both the mandible and thumb. Extensive training with a made-to-fit device has the potential to enable a visual response time equivalent to the fingers in more complex tasks. The mandible is a viable option for accessible HMI for discreet inputs. Further testing into continuous input is needed to explore the mandible’s potential as an input for body augments.
Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
StatePublished - Oct 9 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-12-02
Acknowledgements: This research was supported by members of the Robotics, Intelligent Systems, and Control (RISC) lab at KAUST. Special thanks to Amin Almozel, Brian Parrot, Mohamad Shahab, and Renzo Caballero for editorial help; and to Ashwag Asseri and Ibrahim Alsalamah for helping with recruitment.


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