A Walking Training System with Customizable Trajectory Designing

AbstractThis paper shows a novel walking training system for foot-eye coordination. To design customizable trajectories for different users conveniently in walking training, a new system which can track and record the actual walking trajectories by a tutor and can use these trajectories for the walking training by a trainee is developed. We set the four items as its human-robot interaction design concept: feedback, synchronization, ingenuity and adaptability. A foot model is proposed to define the position and direction of a foot. The errors in the detection method used in the system are less than 40 mm in position and 15 deg in direction. On this basis, three parts are structured to achieve the system functions: Trajectory Designer, Trajectory Viewer and Mobile Walking Trainer. According to the experimental results,we have confirmed the systemworks as intended and designed such that the steps recorded in Trajectory Designer could be used successfully as the footmarks projected in Mobile Walking Trainer and foot-eye coordination training would be conducted smoothly.

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GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽

10.3390/robotics10020068 ◽

2021 ◽

Vol 10 (2) ◽

pp. 68

Author(s):

Lei Shi ◽

Cosmin Copot ◽

Steve Vanlanduit

Keyword(s):

Cognitive Load ◽

Real World ◽

Robotic Manipulator ◽

Similarity Score ◽

Human Robot Interaction ◽

Experimental Results ◽

The Other ◽

Robot Interaction ◽

Run Length ◽

The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

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Should my robot know what's best for me? Human–robot interaction between user experience and ethical design

AI & Society ◽

10.1007/s00146-021-01210-3 ◽

2021 ◽

Author(s):

Nora Fronemann ◽

Kathrin Pollmann ◽

Wulf Loh

Keyword(s):

User Experience ◽

Interaction Design ◽

Real Life ◽

Well Being ◽

Design Principles ◽

Human Robot Interaction ◽

Ethical Challenges ◽

Original Design ◽

Robot Interaction ◽

Ethical Design

AbstractTo integrate social robots in real-life contexts, it is crucial that they are accepted by the users. Acceptance is not only related to the functionality of the robot but also strongly depends on how the user experiences the interaction. Established design principles from usability and user experience research can be applied to the realm of human–robot interaction, to design robot behavior for the comfort and well-being of the user. Focusing the design on these aspects alone, however, comes with certain ethical challenges, especially regarding the user’s privacy and autonomy. Based on an example scenario of human–robot interaction in elder care, this paper discusses how established design principles can be used in social robotic design. It then juxtaposes these with ethical considerations such as privacy and user autonomy. Combining user experience and ethical perspectives, we propose adjustments to the original design principles and canvass our own design recommendations for a positive and ethically acceptable social human–robot interaction design. In doing so, we show that positive user experience and ethical design may be sometimes at odds, but can be reconciled in many cases, if designers are willing to adjust and amend time-tested design principles.

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A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

Technologies ◽

10.3390/technologies6040119 ◽

2018 ◽

Vol 6 (4) ◽

pp. 119 ◽

Cited By ~ 5

Author(s):

Konstantinos Tsiakas ◽

Maria Kyrarini ◽

Vangelis Karkaletsis ◽

Fillia Makedon ◽

Oliver Korn

Keyword(s):

Training Session ◽

Training System ◽

Human Robot Interaction ◽

Design Development ◽

Robot Assisted ◽

Robot Interaction ◽

Training Systems ◽

Robot Perception ◽

Educational Environments ◽

And Behavior

In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.

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