Faculty of Computing

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Author: search.filters.author.Kuo, I. HSubject: search.filters.subject.Robot sensing systems

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Now showing 1 - 4 of 4
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    Improved robot attitudes and emotions at a retirement home after meeting a robot
    (IEEE, 2010-09-13) Stafford, R. Q; Broadbent, E; Jayawardena, C; Unger, U; Kuo, I. H; Igic, A; Wong, R; Kerse, N; Watson, C; MacDonald, B. A
    This study investigated whether attitudes and emotions towards robots predicted acceptance of a healthcare robot in a retirement village population. Residents (n = 32) and staff (n = 21) at a retirement village interacted with a robot for approximately 30 minutes. Prior to meeting the robot, participants had their heart rate and blood pressure measured. The robot greeted the participants, assisted them in taking their vital signs, performed a hydration reminder, told a joke, played a music video, and asked some questions about falls and medication management. Participants were given two questionnaires; one before and one after interacting with the robot. Measures included in both questionnaires were the Robot Attitude Scale (RAS) and the Positive and Negative Affect Schedule (PANAS). After using the robot, participants rated the overall quality of the robot interaction. Both residents and staff reported more favourable attitudes (p <; .05) and decreases in negative affect (p <; .05) towards the robot after meeting it, compared with before meeting it. Pre-interaction emotions and robot attitudes, combined with post-interaction changes in emotions and robot attitudes, were highly predictive of participants' robot evaluations (R = .88, p <; .05). The results suggest both pre-interaction emotions and attitudes towards robots, as well as experience with the robot, are important areas to monitor and address in influencing acceptance of healthcare robots in retirement village residents and staff. The results support an active cognition model that incorporates a feedback loop based on re-evaluation after experience.
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    An efficient programming framework for socially assistive robots based on separation of robot behavior description from execution
    (IEEE, 2013-11-12) Kuo, I. H; Jayawardena, C; MacDonald, B. A
    One of the main challenges in socially assistive robotics is providing flexible and easy-to-use programming tools for users. Unlike other robots, designing socially assistive robots includes the subject-matter-experts (SMEs) from non-engineering disciplines. Therefore, the provided tools should be suitable for users with less programming experience. On the other hand, socially assistive robotic research involves field trials and user-centric studies, in which user and subject matter expert comments are used to improve the robot applications. Therefore, field programmability and customizability are key requirements. This paper presents a programming framework for socially assistive robots, which satisfies the above requirements; programmability by non-experts, field programmability and customizability. The proposed framework has been successfully implemented, deployed, and tested. Some robots with the framework presented in this paper are already in the commercialization pathway.
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    Socially Assistive Robot HealthBot: Design, Implementation, and Field Trials
    (IEEE, 2016-09-01) Jayawardena, C; Kuo, I. H; Broadbent, E; MacDonald, B. A
    Socially assistive robotics is an important emerging research area. Socially assistive robotics is challenging as it is required to move robots out of laboratories and industrial settings to interact with ordinary human beings as peers, which requires social skills. The design process usually requires multidisciplinary research teams, which may comprise subject matter experts from various domains such as robotics, systems integration, medicine, psychology, gerontology, social and cognitive sciences, and neuroscience, among many others. Unlike most other robotic applications, socially assistive robotics faces some unique software and systems integration challenges. In this paper, the HealthBot robot architecture, which was designed to overcome these challenges, is presented. The presented architecture was implemented and used in several field trials. The details of the field trials are presented, and lessons learned are discussed with field trial results.
  • Thumbnail Image
    PublicationEmbargo
    Improved robot attitudes and emotions at a retirement home after meeting a robot
    (IEEE, 2010-09-13) Stafford, R. Q; Broadbent, E; Jayawardena, C; Unger, U; Kuo, I. H; Igic, A; Wong, R; Kerse, N; Watson, C; MacDonald, B. A
    This study investigated whether attitudes and emotions towards robots predicted acceptance of a healthcare robot in a retirement village population. Residents (n = 32) and staff (n = 21) at a retirement village interacted with a robot for approximately 30 minutes. Prior to meeting the robot, participants had their heart rate and blood pressure measured. The robot greeted the participants, assisted them in taking their vital signs, performed a hydration reminder, told a joke, played a music video, and asked some questions about falls and medication management. Participants were given two questionnaires; one before and one after interacting with the robot. Measures included in both questionnaires were the Robot Attitude Scale (RAS) and the Positive and Negative Affect Schedule (PANAS). After using the robot, participants rated the overall quality of the robot interaction. Both residents and staff reported more favourable attitudes (p <; .05) and decreases in negative affect (p <; .05) towards the robot after meeting it, compared with before meeting it. Pre-interaction emotions and robot attitudes, combined with post-interaction changes in emotions and robot attitudes, were highly predictive of participants' robot evaluations (R = .88, p <; .05). The results suggest both pre-interaction emotions and attitudes towards robots, as well as experience with the robot, are important areas to monitor and address in influencing acceptance of healthcare robots in retirement village residents and staff. The results support an active cognition model that incorporates a feedback loop based on re-evaluation after experience.