Martin Lochner

Visual and Manual Control for Human-Robot Teleoperation

In an effort to assess simple control modalities for a remote robotic system, this study explores and tests the suitability of four interfaces for teleoperation in human-robot interaction. For a pick-and-place task, users were asked to select targets and locations using eye tracking (activated by either a mouse click or dwell time), a touchscreen, or a standard computer mouse. Contrary to their expectations, the authors found that eye-tracking-based interaction, especially when paired with manual-click selection, was generally slower and was perceived as more difficult than the mouse and touchscreen interfaces. Conversely, as predicted, they found evidence that eye tracking with dwell selection was less prone to interference caused by a secondary manual task.

Detecting Intention Through Motor-Imagery-Triggered Pupil Dilations

Human–computer interaction systems that bypass manual control can be beneficial for many use cases, including users with severe motor disability. We investigated pupillometry (inferring mental activity via dilations of the pupil) as an interaction method because it is noninvasive, easy to analyse, and increasingly available for practical development. In 3 experiments we investigated the efficacy of using pupillometry to detect imaginary motor movements of the hand. In Experiment 1 we demonstrated that, on average, the pupillary response is greater when the participant is imagining a hand-grasping motion, as compared with the control condition. In Experiment 2 we investigated how imaginary hand-grasping affects the pupillary response over time. In Experiment 3 we employed a simple classifier to demonstrate single-trial detection of imagined motor events using pupillometry. Using the mean pupil diameter of a single trial, accuracy rates as high as 71.25%, were achieved. Implications for the development of a pupillometry-based switch and future directions are discussed.