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Article

Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management

1
Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
2
Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK 10250, Pakistan
3
L3S Research Center, Leibniz Universität, 30167 Hannover, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 569; https://doi.org/10.3390/s20020569
Received: 30 November 2019 / Revised: 16 January 2020 / Accepted: 18 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Sensors for Affective Computing and Sentiment Analysis)
Coping with stress is crucial for a healthy lifestyle. In the past, a great deal of research has been conducted to use socially assistive robots as a therapy to alleviate stress and anxiety related problems. However, building a fully autonomous social robot which can deliver psycho-therapeutic solutions is a very challenging endeavor due to limitations in artificial intelligence (AI). To overcome AI’s limitations, researchers have previously introduced crowdsourcing-based teleoperation methods, which summon the crowd’s input to control a robot’s functions. However, in the context of robotics, such methods have only been used to support the object manipulation, navigational, and training tasks. It is not yet known how to leverage real-time crowdsourcing (RTC) to process complex therapeutic conversational tasks for social robotics. To fill this gap, we developed Crowd of Oz (CoZ), an open-source system that allows Softbank’s Pepper robot to support such conversational tasks. To demonstrate the potential implications of this crowd-powered approach, we investigated how effectively, crowd workers recruited in real-time can teleoperate the robot’s speech, in situations when the robot needs to act as a life coach. We systematically varied the number of workers who simultaneously handle the speech of the robot (N = 1, 2, 4, 8) and investigated the concomitant effects for enabling RTC for social robotics. Additionally, we present Pavilion, a novel and open-source algorithm for managing the workers’ queue so that a required number of workers are engaged or waiting. Based on our findings, we discuss salient parameters that such crowd-powered systems must adhere to, so as to enhance their performance in response latency and dialogue quality. View Full-Text
Keywords: social robotics; coaching; social conversation; crowdsourcing; human computation; real-time crowd-powered systems; stress social robotics; coaching; social conversation; crowdsourcing; human computation; real-time crowd-powered systems; stress
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MDPI and ACS Style

Abbas, T.; Khan, V.-J.; Gadiraju, U.; Barakova, E.; Markopoulos, P. Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management. Sensors 2020, 20, 569. https://doi.org/10.3390/s20020569

AMA Style

Abbas T, Khan V-J, Gadiraju U, Barakova E, Markopoulos P. Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management. Sensors. 2020; 20(2):569. https://doi.org/10.3390/s20020569

Chicago/Turabian Style

Abbas, Tahir; Khan, Vassilis-Javed; Gadiraju, Ujwal; Barakova, Emilia; Markopoulos, Panos. 2020. "Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management" Sensors 20, no. 2: 569. https://doi.org/10.3390/s20020569

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