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ItemUnderstanding older adults' perceptions of usefulness of an assistive home robot(Georgia Institute of Technology, 2013-11-21) Beer, Jenay M.Developing robots that are useful to older adults is more than simply creating robots that complete household tasks. To ensure that older adults perceive a robot to be useful, careful consideration of the users’ capabilities, robot autonomy, and task is needed (Venkatesh & Davis, 2000). The purpose of this study was to investigate the construct of perceived usefulness within the context of robot assistance. Mobile older adults (N = 12) and older adults with mobility loss (N=12) participated in an autonomy selection think aloud task, and a persona based interview. Findings suggest that older adults with mobility loss preferred an autonomy level where they command/control the robot themselves. Mobile older adults’ preferences were split between commanding/controlling the robot themselves, or the robot commands/controls itself. Reasons for their preferences were related to decision making, and were task specific. Additionally, findings from the persona base interview study support Technology Acceptance Model (TAM) constructs, as well as adaptability, reliability, and trust as positively correlated with perceptions of usefulness. However, despite the positive correlation, barriers and facilitators of acceptance identified in the interview suggest that perceived usefulness judgments are complex, and some questionnaire constructs were interpreted differently between participants. Thus, care should be taken when applying TAM constructs to other domains, such as robot assistance to promote older adult independence.
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ItemUnderstanding the construct of human trust in domestic service robots(Georgia Institute of Technology, 2013-11-18) Olson, Katherine E.Simple robots are already being deployed and adopted by some consumers for use at home. The robots currently in development for home use are far more sophisticated. However, it was not know the extent to which humans would trust them. The purpose of this study was to identify factors that influence trust in domestic service robots across a range of users with different capabilities and experience levels. Twelve younger adults (aged 18-28) and 24 older adults (12 low technology users and 12 high technology users) aged 65-75 participated in a structured interview, card-sorting task, and several questionnaires. Most participants had heard about or seen robots, but indicated they had little experience with them. However, most had positive opinions about robots and indicated they would trust a robot to assist with tasks in their homes, though it was dependent on the task. Before making a decision to trust a robot, participants wanted to know a lot of information about the robot such robot reliability, capabilities, and limitations. When asked to select their trust preference for human versus robot assistance for specific tasks, participants had preferences for both human and robot assistance, although it was dependent on the task. Many participants defined trust in robots similar to definitions of trust in automation (Ezer, 2008; Jian et al., 2000). Additionally, they had high rates of selection for adjectives used to describe trust in automation and also selected some adjectives used to describe trust in humans when asked to select characteristics they most associated with trustworthy and untrustworthy robots. Overall, there were some differences between age and technology experience groups, but there were far more similarities. By carefully considering user needs, robot designers can develop robots that have the potential to be adopted by a wide range of people.
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ItemEmotion and motion: age-related differences in recognizing virtual agent facial expressions(Georgia Institute of Technology, 2011-10-05) Smarr, Cory-AnnTechnological advances will allow virtual agents to increasingly help individuals with daily activities. As such, virtual agents will interact with users of various ages and experience levels. Facial expressions are often used to facilitate social interaction between agents and humans. However, older and younger adults do not label human or virtual agent facial expressions in the same way, with older adults commonly mislabeling certain expressions. The dynamic formation of facial expression, or motion, may provide additional facial information potentially making emotions less ambiguous. This study examined how motion affects younger and older adults in recognizing various intensities of emotion displayed by a virtual agent. Contrary to the dynamic advantage found in emotion recognition for human faces, older adults had higher emotion recognition for static virtual agent faces than dynamic ones. Motion condition did not influence younger adults' emotion recognition. Younger adults had higher emotion recognition than older adults for the emotions of anger, disgust, fear, happiness, and sadness. Low intensities of expression had lower emotion recognition than medium to high expression intensities.
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ItemRecognizing facial expression of virtual agents, synthetic faces, and human faces: the effects of age and character type on emotion recognition(Georgia Institute of Technology, 2010-04-08) Beer, Jenay MichelleAn agent's facial expression may communicate emotive state to users both young and old. The ability to recognize emotions has been shown to differ with age, with older adults more commonly misidentifying the facial emotions of anger, fear, and sadness. This research study examined whether emotion recognition of facial expressions differed between different types of on-screen agents, and between age groups. Three on-screen characters were compared: a human, a synthetic human, and a virtual agent. In this study 42 younger (age 28-28) and 42 older (age 65-85) adults completed an emotion recognition task with static pictures of the characters demonstrating four basic emotions (anger, fear, happiness, and sadness) and neutral. The human face resulted in the highest proportion match, followed by the synthetic human, then the virtual agent with the lowest proportion match. Both the human and synthetic human faces resulted in age-related differences for the emotions anger, fear, sadness, and neutral, with younger adults showing higher proportion match. The virtual agent showed age-related differences for the emotions anger, fear, happiness, and neutral, with younger adults showing higher proportion match. The data analysis and interpretation of the present study differed from previous work by utilizing two unique approaches to understanding emotion recognition. First, misattributions participants made when identifying emotion were investigated. Second, a similarity index of the feature placement between any two virtual agent emotions was calculated, suggesting that emotions were commonly misattributed as other emotions similar in appearance. Overall, these results suggest that age-related differences transcend human faces to other types of on-screen characters, and differences between older and younger adults in emotion recognition may be further explained by perceptual discrimination between two emotions of similar feature appearance.