TY - GEN
T1 - Enhancing the Supervision of Out-of-View Robots
T2 - 2023 Mensch und Computer Conference, MuC 2023
AU - Kassem, Khaled
AU - Shahu, Ambika
AU - Tüchler, Christina
AU - Wintersberger, Philipp
AU - Michahelles, Florian
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/9/3
Y1 - 2023/9/3
N2 - Objective: investigating the effect of two support methods (multimodal feedback, monitoring screens, and a combination of both) on human dual-task performance, cognitive workload, and user experience when supervising an out-of-sight autonomous robot. Method: A 2x2 within-group user study was conducted in VR with 26 participants involving a cognitive-cognitive dual-task setting. Participants had to simultaneously solve math problems and supervise the robot. Different support methods were provided: multimodal feedback, a screen showing real-time robot activity, and a combination of both. Objective performance metrics and subjective feedback on cognitive load and user experience were collected using standard questionnaires. Data were statistically analyzed, and thematic analysis was performed on post-study debriefing interviews. Results: The support methods improved overall user experience and positively impacted robot collaboration performance while decreasing math task performance. Cognitive load was unaffected. Multimodal feedback with a monitoring screen was perceived as the most helpful. Conclusion: The results suggest that multimodal feedback can improve user experience and improve supervision, but may partially decrease primary task performance. The findings highlight the importance of examining the effect of support methods in specific situations, depending on task priority.
AB - Objective: investigating the effect of two support methods (multimodal feedback, monitoring screens, and a combination of both) on human dual-task performance, cognitive workload, and user experience when supervising an out-of-sight autonomous robot. Method: A 2x2 within-group user study was conducted in VR with 26 participants involving a cognitive-cognitive dual-task setting. Participants had to simultaneously solve math problems and supervise the robot. Different support methods were provided: multimodal feedback, a screen showing real-time robot activity, and a combination of both. Objective performance metrics and subjective feedback on cognitive load and user experience were collected using standard questionnaires. Data were statistically analyzed, and thematic analysis was performed on post-study debriefing interviews. Results: The support methods improved overall user experience and positively impacted robot collaboration performance while decreasing math task performance. Cognitive load was unaffected. Multimodal feedback with a monitoring screen was perceived as the most helpful. Conclusion: The results suggest that multimodal feedback can improve user experience and improve supervision, but may partially decrease primary task performance. The findings highlight the importance of examining the effect of support methods in specific situations, depending on task priority.
UR - http://www.scopus.com/inward/record.url?scp=85171153483&partnerID=8YFLogxK
U2 - 10.1145/3603555.3608550
DO - 10.1145/3603555.3608550
M3 - Conference contribution
AN - SCOPUS:85171153483
T3 - ACM International Conference Proceeding Series
SP - 487
EP - 491
BT - Mensch und Computer 2023
A2 - Stolze, Markus
A2 - Loch, Frieder
A2 - Baldauf, Matthias
A2 - Alt, Florian
A2 - Schneegass, Christina
A2 - Kosch, Thomas
A2 - Hirzle, Teresa
A2 - Sadeghian, Shadan
A2 - Draxler, Fiona
A2 - Bektas, Kenan
A2 - Lohan, Katrin
A2 - Knierim, Pascal
PB - Association for Computing Machinery
Y2 - 3 September 2023 through 6 September 2023
ER -