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IntroductionAs robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective. MethodsThis study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N= 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements. ResultsOur results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned. DiscussionThese findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.
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A Region-of-Interest-Reweight 3D Convolutional Neural Network for the Analytics of Brain Information Processing
We study how human brains activate to process input information and execute necessary cognitive tasks. Understanding the process is crucial in improving our diagnostic and treatment of different neurological disorders. Given functional MRI images recorded when human subjects execute tasks with different levels of information uncertainty, we need to identify the similarity and difference between brain activities at different regions of interest (ROIs), and thus gain insights into the underlying mechanism. To achieve this goal, we propose a new ROI- reweight 3D convolutional neural network (CNN). Our CNN not only learns to classify the task-evoked fMRIs with a high accuracy, but also locates crucial ROIs based on a reweight layer. Our findings reveal several brain regions to be crucial in differentiating brain activity patterns facing tasks of different uncertainty levels.
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- Award ID(s):
- 1718802
- PAR ID:
- 10066720
- Date Published:
- Journal Name:
- the 21st International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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