scholarly journals Human Machine Interface in Assistive Robotics: Application to a Force Controlled Upper-Limb Powered Exoskeleton

10.5772/7329 ◽  
2010 ◽  
Author(s):  
Malek Baklouti ◽  
Jamil AbouSaleh ◽  
Eric Monacelli ◽  
Serge Couvet
Keyword(s):  
Upper Limb ◽  
Human Machine Interface ◽  
Assistive Robotics ◽  
Machine Interface ◽  
Powered Exoskeleton

scholarly journals A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

2020 ◽  
Vol 14 ◽  
Author(s):  
Yonghao Song ◽  
Siqi Cai ◽  
Lie Yang ◽  
Guofeng Li ◽  
Weifeng Wu ◽  
...  
Keyword(s):  
Upper Limb ◽  
Online Control ◽  
Human Machine Interface ◽  
Machine Interface

scholarly journals Decision-Making in the Human-Machine Interface

2021 ◽  
Vol 12 ◽  
Author(s):  
J. Benjamin Falandays ◽  
Samuel Spevack ◽  
Philip Pärnamets ◽  
Michael Spivey
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Human Machine Interface ◽  
Assistive Robotics ◽  
Clear Understanding ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Directional Bias ◽  
Model Simulations ◽  
Machine Interface

If our choices make us who we are, then what does that mean when these choices are made in the human-machine interface? Developing a clear understanding of how human decision making is influenced by automated systems in the environment is critical because, as human-machine interfaces and assistive robotics become even more ubiquitous in everyday life, many daily decisions will be an emergent result of the interactions between the human and the machine – not stemming solely from the human. For example, choices can be influenced by the relative locations and motor costs of the response options, as well as by the timing of the response prompts. In drift diffusion model simulations of response-prompt timing manipulations, we find that it is only relatively equibiased choices that will be successfully influenced by this kind of perturbation. However, with drift diffusion model simulations of motor cost manipulations, we find that even relatively biased choices can still show some influence of the perturbation. We report the results of a two-alternative forced-choice experiment with a computer mouse modified to have a subtle velocity bias in a pre-determined direction for each trial, inducing an increased motor cost to move the cursor away from the pre-designated target direction. With queries that have each been normed in advance to be equibiased in people’s preferences, the participant will often begin their mouse movement before their cognitive choice has been finalized, and the directional bias in the mouse velocity exerts a small but significant influence on their final choice. With queries that are not equibiased, a similar influence is observed. By exploring the synergies that are developed between humans and machines and tracking their temporal dynamics, this work aims to provide insight into our evolving decisions.

Designing the human machine interface in the ATIS domain

1990 ◽  
Author(s):  
B. Bly ◽  
P. J. Price ◽  
S. Park ◽  
S. Tepper ◽  
E. Jackson ◽  
...  
Keyword(s):  
Human Machine Interface ◽  
Machine Interface

scholarly journals Evaluation of Multimodal External Human–Machine Interface for Driverless Vehicles in Virtual Reality

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 687
Author(s):  
Jinzhen Dou ◽  
Shanguang Chen ◽  
Zhi Tang ◽  
Chang Xu ◽  
Chengqi Xue
Keyword(s):  
New Technology ◽  
Pedestrian Safety ◽  
Human Machine Interface ◽  
Visual Features ◽  
Visual Modality ◽  
Human Voice ◽  
Road Users ◽  
Machine Interface ◽  
Interaction Efficiency ◽  
Physical Features

With the development and promotion of driverless technology, researchers are focusing on designing varied types of external interfaces to induce trust in road users towards this new technology. In this paper, we investigated the effectiveness of a multimodal external human–machine interface (eHMI) for driverless vehicles in virtual environment, focusing on a two-way road scenario. Three phases of identifying, decelerating, and parking were taken into account in the driverless vehicles to pedestrian interaction process. Twelve eHMIs are proposed, which consist of three visual features (smile, arrow and none), three audible features (human voice, warning sound and none) and two physical features (yielding and not yielding). We conducted a study to gain a more efficient and safer eHMI for driverless vehicles when they interact with pedestrians. Based on study outcomes, in the case of yielding, the interaction efficiency and pedestrian safety in multimodal eHMI design was satisfactory compared to the single-modal system. The visual modality in the eHMI of driverless vehicles has the greatest impact on pedestrian safety. In addition, the “arrow” was more intuitive to identify than the “smile” in terms of visual modality.

SOLI: A Tiny Device for a New Human Machine Interface

2021 ◽  
Author(s):  
Saverio Trotta ◽  
Dave Weber ◽  
Reinhard W. Jungmaier ◽  
Ashutosh Baheti ◽  
Jaime Lien ◽  
...  
Keyword(s):  
Human Machine Interface ◽  
Machine Interface
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