Online stress detection using psychophysiological signals for implicit human-robot cooperation

Rani, P. Arockia Jansi; Sims, J. Paul; Brackin, Robert; Sarkar, Nilanjan

doi:10.1017/s0263574702004484

Cited by 126 publications

(84 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HRV was previously used as a major marker for stress [36] and has also been shown to decrease with increased physical effort [37], which is the case in treadmill walking. Physical effort of walking might have occluded psychological influence of stress on HRV, which is why HRV might not have changed significantly in our subjects.…”

Section: Influence Of Physical Activity Neurological Deficits and Mmentioning

confidence: 99%

See 1 more Smart Citation

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

Koenig

Omlin

Zimmerli

et al. 2011

JRRD

View full text Add to dashboard Cite

Abstract-Robot-assisted treadmill training is an established intervention used to improve walking ability in patients with neurological disorders. Although it has been shown that attention to the task is a key factor for successful rehabilitation, the psychological state of patients during robot-assisted gait therapy is often neglected. We presented 17 nondisabled subjects and 10 patients with neurological disorders a virtual-reality task with varying difficulty levels to induce feelings of being bored, excited, and overstressed. We developed an approach to automatically estimate and classify a patient's psychological state, i.e., his or her mental engagement, in real time during gait training. We used psychophysiological measurements to obtain an objective measure of the current psychological state. Automatic classification was performed by a neural network. We found that heart rate, skin conductance responses, and skin temperature can be used as markers for psychological states in the presence of physical effort induced by walking. The classifier achieved a classification error of 1.4% for nondisabled subjects and 2.1% for patients with neurological disorders. Using our new method, we processed the psychological state data in real time. Our method is a first step toward real-time auto-adaptive gait training with potential to improve rehabilitation results by optimally challenging patients at all times during exercise.

show abstract

Section: Influence Of Physical Activity Neurological Deficits and Mmentioning

confidence: 99%

“…Rani et al performed closed-loop control of mental stress in sitting nondisabled subjects and used changes in HRV and GSR for classification of the stress level [36]. Prinzel et al used the electroencephalogram to moderate an operator's level of task engagement through a closed-loop system [43].…”

Section: Real-time Capability Of Approachmentioning

confidence: 99%

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

Koenig

Omlin

Zimmerli

et al. 2011

JRRD

View full text Add to dashboard Cite

show abstract

“…In the same vein, in order to assess the efficacy of HRI during robot therapy interventions for demented patients, Wada et al [11] acquired patients' EEG signals. Furthermore, Rani et al [12] presented a novel methodology for online stress detection based on heart rate measurement, with the aim to give the robot the ability to understand the user's anxiety during cooperative tasks.…”

Section: Introductionmentioning

confidence: 99%

Measuring Human-Robot Interaction Through Motor Resonance

Sciutti

Bisio

Nori

et al. 2012

Int J of Soc Robotics

View full text Add to dashboard Cite

In the last decades, the introduction of robotic devices in fields such as industries, dangerous environments, and medicine has notably improved working practices. The availability of a new generation of humanoid robots for everyday's activities in human populated environments can entail an even wider revolution. Indeed, not only domestic activities but also social behaviors will adapt to a continuous interaction with a completely new kind of social agents.In the light of this scenario, it becomes crucial to design robots suited to natural cooperation with humans, and contextually to develop quantitative methods to measure human-robot interaction (HRI). Motor resonance, i.e. the activation of the observer's motor control system during action perception, has been suggested to be a key component of human social behavior, and as such is thought to play a central role for HRI.A. Sciutti and A. Bisio contributed equally to this work. In the literature there are reports of robots that have been used as tools to understand the human brain. The aim of this review is to offer a different perspective in suggesting that human responses can become a tool to measure and improve robot interactional attitudes. In the first part of the paper the notion of motor resonance and its neurophysiological correlates are introduced. Subsequently we describe motor resonance studies on the perception of robotic agents' behavior. Finally we introduce proactive gaze and automatic imitation, two techniques adopted in human motor resonance studies, and we present the advantages which would follow their application to HRI.

show abstract

“…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%

“…Signals proposed for use in human-computer interfaces include skin conductance, heart rate, pupil dilation and brain and muscle neural activity. Rani et al [9,11] used heart-rate analysis and multiple physiological signals to estimate human stress levels during video game playing, using a fuzzy inference engine. Kulic and Croft [13] used a fuzzy inference engine to estimate affective state response to articulated robot motions.…”

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

View full text Add to dashboard Cite

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.

show abstract

Online stress detection using psychophysiological signals for implicit human-robot cooperation

Cited by 126 publications

References 17 publications

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

Measuring Human-Robot Interaction Through Motor Resonance

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

Contact Info

Product

Resources

About