Next Article in Journal
Study of Metal–Semiconductor–Metal CH3NH3PbBr3 Perovskite Photodetectors Prepared by Inverse Temperature Crystallization Method
Next Article in Special Issue
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
Previous Article in Journal
Influence of Electrostatic Forces on the Vibrational Characteristics of Resonators for Coriolis Vibratory Gyroscopes
Previous Article in Special Issue
Adaptive Binarization of QR Code Images for Fast Automatic Sorting in Warehouse Systems
Article

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

ISAE-SUPAERO, Université de Toulouse, 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 296; https://doi.org/10.3390/s20010296
Received: 2 December 2019 / Revised: 31 December 2019 / Accepted: 2 January 2020 / Published: 5 January 2020
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
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. View Full-Text
Keywords: human–robot interaction; physiological computing; intelligent sensors; performance prediction; human behavior human–robot interaction; physiological computing; intelligent sensors; performance prediction; human behavior
Show Figures

Figure 1

MDPI and ACS Style

Chanel, C.P.C.; Roy, R.N.; Dehais, F.; Drougard, N. Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction. Sensors 2020, 20, 296. https://doi.org/10.3390/s20010296

AMA Style

Chanel CPC, Roy RN, Dehais F, Drougard N. Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction. Sensors. 2020; 20(1):296. https://doi.org/10.3390/s20010296

Chicago/Turabian Style

Chanel, Caroline P.C., Raphaëlle N. Roy, Frédéric Dehais, and Nicolas Drougard. 2020. "Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction" Sensors 20, no. 1: 296. https://doi.org/10.3390/s20010296

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop