Considering human's non-deterministic behavior and his availability state when designing a collaborative human-robots system

Gateau, Thibault; Chanel, Caroline Ponzoni Carvalho; Le, Mai-Huy; Dehais, Frédéric

doi:10.1109/iros.2016.7759646

Cited by 20 publications

(36 citation statements)

References 19 publications

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“…The second category of neuroadaptive countermeasure is the dynamic reallocation of tasks between humans and automation to maintain the performance efficacy of the operators (Freeman et al, 1999;Parasuraman et al, 1999;Prinzel et al, 2000;Scerbo, 2008;Stephens et al, 2018). The underlying concept in this case is to optimize human-human or human(s)-system(s) cooperation according to criteria of availability and skills of human and artificial agents (Gateau et al, 2016). For instance, Prinzel et al (2000) utilized the continuous monitoring of brain waves that could be used to drive the level of automation and optimize the user's level of task engagement.…”

Section: Task and Automation Adaptationmentioning

confidence: 99%

“…These latter studies reported better human performance when neuro-adaptive automation was switched on compared to other conditions. Gateau et al (2016) implemented an online attentional state estimator coupled with a stochastic decision framework to dynamically adapt authority sharing between human and robots in a search and rescue scenario to prevent effort withdrawal on the part of the human. In a more extreme fashion, Callan et al (2016) revealed that it is possible to decode user motor intention so automation can perform on behalf of the user to drastically reduce the response time in emergency situations (e.g., collision with terrain).…”

Section: Task and Automation Adaptationmentioning

confidence: 99%

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A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

et al. 2020

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The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a twodimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

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Section: Task and Automation Adaptationmentioning

confidence: 99%

Section: Task and Automation Adaptationmentioning

confidence: 99%

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

et al. 2020

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“…To go further, an interesting point would be to consider more levels of automation and their impact on the physiological and on the behavioral performance features. Sliding autonomy [45] literature gives examples where human operators could deliberatly choose the control mode [46], could be solicited to identify targets [4,5], or indicate to the system their intention of taking manual control [34,47]. Interestingly, the authors of [48] studied the impact on the human operator performance considering three levels of automation in a multi-robot scenario.…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

“…Beyond that, and based on the achieved results of this paper, the next step in this research is to implement online methods to continuously infer the human operators' performance like-profile (low versus high performer) and to launch adaptation based on the classification results obtained possibly used to feed a sequential decision-making process. More precisely, we believe that such an online performance profile prediction could be used to feed a stochastic sequential decision-making model dedicated to trigger adaptation [4,12].…”

Section: Future Researchmentioning

confidence: 99%

“…In the near future, human and artificial agents will be deployed to handle several missions in different contexts working as teammates. Some examples of contexts for which mixed human-machine systems would be relevant are search and rescue missions [1]; dirty, dull, and dangerous (3D) environments [2]; Mars rovers [3]; and multi-UAVs operation [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Chanel

Roy

Dehais

et al. 2020

Sensors

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The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.

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Neuroergonomics

Ayaz

Dehais

2021

Handbook of Human Factors and Ergonomics

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Considering human's non-deterministic behavior and his availability state when designing a collaborative human-robots system

Cited by 20 publications

References 19 publications

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Neuroergonomics

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