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Abstract Human–exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human–exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human–exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human–exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.
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Designing and Using Capture The Flag for Coordination and Interaction in Engineering Education
Capture the Flag (CTF) games improve learners’ engagement and diversify pedagogy for education and training. We design and build a novel CTF game that includes coordination and interaction between the (virtually participating) participants to build fellowship and facilitate networking. Our work builds on the existing CTF components with educational benefits but differs from the traditional CTF approach which presents either an individual game with no participant interaction or a team-based game where the members already know each other and have formed teams. More specifically, we incorporate real-time interactions between participants who are new to each other and engage the participants to collectively solve the CTF challenges. We apply our CTF in both a cybersecurity scholarship program and an academic conference. This paper describes and explains the design, implementation, execution, and validation of our CTF, particularly focusing on the novel goal of including coordination and interaction in order to build fellowships with the participants. We validate our CTF design and build using multiple channels, including the real-time data provided by logging during the session, post-CTF survey, and interviews from the beta-testing session. Our evaluation results show that our novel CTF focusing on coordination and interaction aids in building fellowship and a collaborative environment. We envision our CTF design to help with the rapport building and collaboration among participants in classroom/course settings, workshops, conferences, or technical training sessions.
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- Award ID(s):
- 1922410
- PAR ID:
- 10423196
- Date Published:
- Journal Name:
- IEEE Global Engineering Education Conference (EDUCON)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EDUCON52537.2022.9766724
