Vibrotactile and Force Collaboration within 3D Virtual Environments

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID Abstract-In a three-dimensional (3D) virtual environment (VE), proper collaboration between vibrotactile and force cuestwo cues of the haptic modality -is important to facilitate task performance of human users.Many studies report that collaborations between multi-sensory cues follow maximum likelihood estimation (MLE). However, an existing work finds that MLE yields a mean and an amplitude mismatches when interpreting the collaboration between the vibrotactile and force cues. We thus proposed mean-shifted MLE and conducted a human study to investigate the mismatches. For the study, we created a VE to replicate the visual scene, the 3D interactive task, and the cues from the existing work. Our participants were biased to rely on the vibrotactile cue for their tasks, departing from unbiased reliance on both cues in the existing work. Assessments of task completion time and task accuracy validated the replication. We found that based on task accuracy MLE explained the cue collaboration to certain degrees, agreed with the existing work. Mean-shifted MLE remedied the mean mismatch, but maintained the amplitude mismatch. Further examinations revealed that the collaboration between both cues may not be entirely additive. This sheds an insight for proper modeling of the collaboration between the vibrotactile and force cues to aid interactive tasks in VEs.

Towards EEG-Based Haptic Interaction within Virtual Environments

“…All vibration-related blocks exhibited no difference among their percentages of the responses. The findings related to the percentage of the correct responses agreed with those in prior work [10].…”

“…The feasibility study applied a 3D stereoscopic VE -developed with Unity 3D (5.3.2f1) -for a participant to perform an interactive task of flying a virtual drone along a curved transmission line [10]. Six male healthy volunteers (age of 23.8 ± 2.7 years old and naïve to the purpose of the study) participated in the study.…”

“…Raw EEG data were bandpass filtered at 1-35Hz to eliminate commonly known artifacts [9]. The filtered data were then analyzed by applying custom MATLAB scripts utilizing EEGLAB v13.4 [10]. As depicted in Fig.…”