A Framework for Monitoring Human Physiological Response during Human Robot Collaborative Task

Abstract

In this paper, a framework for monitoring human physiological response during Human-Robot Collaborative (HRC) task is presented. The framework highlights the importance of generation of event markers related to both human and robot, and also synchronization of data collected. This framework enables continuous data collection during an HRC task when changing robot movements as a form of stimuli to invoke a human physiological response. It also presents two case studies based on this framework and a data visualization tool for representation and easy analysis of the collected data during an HRC experiment.

Human factors in cobot era: a review of modern production systems features

“…Similarly, a compliant joint or an arm in the robot has been proved to reduce injury that leads to better confidence of operators (She et al, 2020) . Furthermore, (Savur et al, 2019) added that any potential physical collision with the robot decreases the human trust, and consequently the production benefits of the HRC. On the other hand, (Sauppé & Mutlu, 2015) found that the social features in industrial robots may reduce the safety of the interaction.…”

“…The adaptability of the robot has also been used to improve the acceptance and the trust of the operator. As stated by (Savur et al, 2019) , trust is about managing human expectations; therefore, the authors developed a system to monitor human physiological responses to measure trust and suitably adapting the robot speed, acceleration and trajectory. As seen in (Lasota & Shah, 2015) , improving the acceptance and trust of the operator leads to reduced idle times and improved productivity, similar to improving physical ergonomics.…”

“…(Pohlt et al, 2018) compared the user experience with gesture inputs and touch feedback, showing that the users preferred the latter, despite the performance of the system is similar in both scenarios, with similar task completion time. Other systems have been considered, such as voice-based systems (Nordqvist & Lindblom, 2018;Savur et al, 2019) or graphical supports (Eimontaite et al, 2019;Tan et al, 2009) , which have been adopted to provide feedback to the operator. In particular, in (Eimontaite et al, 2019) static graphical signs have been adopted since it displays information with little to no prior experience and does not depend on the language.…”

Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction

“…Collaborative robots, specifically in tasks where the user and the robot take independent actions that jointly manipulate the environment towards a mutual goal state, also perform environmental assistance. Examples of such systems include collaborative cleaning (Devin and Alami, 2016) and assembly (Savur et al, 2019;Zhao et al, 2020). A robot working collaboratively with a user can improve its efficiency by modeling the user's behavior, for example by determining specific poses to hold an object in to facilitate fluid collaboration during assembly (Akkaladevi et al, 2016) or by anticipating and delivering the next required item in assembly (Hawkins et al, 2013(Hawkins et al, , 2014Maeda et al, 2014) or cooking (Koppula et al, 2016;Milliez et al, 2016), or by providing help under different initiative paradigms during assembly (Baraglia et al, 2016).…”

Simultaneous prediction of valence / arousal and emotion categories and its application in an HRC scenario

“…Seo et al (2019) used machine learning to predict boredom from electroencephalogram data (EEG). Savur et al (2019) used EEG, Electrocardiogram (ECG), Electromyography (EMG), Galvanic Skin Response (GSR), Heart Rate (HR), Heart Rate Bariability (HRV), and pupil dilation to adjust robot speed within HCR-scenarios. Höfling et al (2020) compared biophysiological measurements with the output of commercially available facial analysis software (FaceReader).…”