Anxiety detecting robotic system – towards implicit human-robot collaboration

Rani, P. Arockia Jansi; Sarkar, Nilanjan; Smith, Craig A.; Kirby, Leslie D.

doi:10.1017/s0263574703005319

Cited by 143 publications

(107 citation statements)

References 33 publications

(27 reference statements)

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“…This method has been used by [13], [12], and [14] to measure anxiety and stress. Delaney and Brodie [12] verified that the increase in HRV was reflective of both self reports and physical tension.…”

Section: Heart Rate Variability As An Additional Measurementioning

confidence: 99%

“…Rowe, Sibert, and Irwin [15], in their study of a aircraft control tower interface, state that "HRV appeared to indicate the point at which user capacity to process targets was exceeded." Within the HRI community, Rani and Sarkar [13], [16] have used HRV as a measure of human anxiety in order to facilitate better responses from a helper robot and identified a relationship between self-reported and measured anxiety levels.…”

Section: Heart Rate Variability As An Additional Measurementioning

confidence: 99%

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Comfortable approach distance with small Unmanned Aerial Vehicles

Duncan

Murphy

2013

2013 Ieee Ro-Man

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Abstract-This paper presents the first known humansubject study of comfortable approach distance and height for human interaction with a small unmanned aerial vehicle (sUAV), finding no conclusive difference in comfort with a sUAV approaching a human at above head height or below head height. Understanding the amount, if any, of discomfort introduced by a sUAV flying in close proximity to a human is critical for law enforcement, crowd control, entertainment, or flying personal assistants. Previous work has focused on how humans interact with each other or with unmanned ground vehicles, and the experimental methods typically rely on the human participant to consciously express distress. The approach taken was to duplicate the experimental set up in human proxemics studies, while adding psychophysiological sensing, under the hypothesis that human-robot interaction will mirror human-human interaction. The 16 participant, within-subjects experiment did not confirm this hypothesis. Instead a sUAV above height of a "tall" person in human experiments (2.13 m) did not produce statistically different heart rate variability nor cause the participant to stop the robot further away than for a sUAV at a "short" height (1.52 m). The lack of effect may be due to two possible confounds: i) duplicating prior human proxemics experiments did not capture how a sUAV would likely move or interact and ii) telling the participants that the robot could not hurt them. Despite possible confounding, the results raise the question of whether humanhuman psychological and physical distancing behavior transfers to human-aerial robot interactions.

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Section: Heart Rate Variability As An Additional Measurementioning

confidence: 99%

Section: Heart Rate Variability As An Additional Measurementioning

confidence: 99%

Comfortable approach distance with small Unmanned Aerial Vehicles

Duncan

Murphy

2013

2013 Ieee Ro-Man

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“…Physiological monitoring systems have previously been used to extract information about the user's reaction, both for human-computer and human-robot interaction [8][9][10][11][12]. Signals proposed for use in human-computer interfaces include skin conductance, heart rate, pupil dilation and brain and muscle neural activity.…”

Section: Duringmentioning

confidence: 99%

Evaluation of affective state estimations using an on-line reporting device during human-robot interactions

Zoghbi

Croft

Kulić

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In order to develop a friendly and safe interaction between humans and robots, it is essential for the robot to evaluate user's affective states and respond accordingly. However, affective states are typically assessed using offline questionnaires and user reports. In this paper we investigate the use of an online-device for collecting real-time user reports of affective state during interaction with a robot. These reports are compared to both previous survey reports taken after the interaction, and the affective states estimated by an inference system. The aim is to evaluate and characterize the physiological signal-based inference system and determine which factors significantly influence its performance. This analysis will be used in future work, to fine tune the affective estimations by identifying what kind of variations in physiological signals precede or accompany the variations in reported affective states.

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“…There is a growing consensus that endowing an intelligent system with an ability to understand implicit affective cues should permit more meaningful and natural HCI and HRI (Picard, 1997). There are several modalities such as facial expression (Bartlett et al, 2003), vocal intonation (Lee & Narayanan, 2005), gestures and postures (Asha et al, 2005;Kleinsmith et al, 2005), and physiology (Kulic & Croft, 2007;Liu et al, 2006;Mandryk & Atkins, 2007;Picard et al, 2001;Rani et al, 2004) that can be utilized to evaluate the affective states. In this work we chose to create affective models based on physiological data for several reasons.…”

Section: Fig 1 Framework Overviewmentioning

confidence: 99%