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- Billing, Erik, PhD, 1981-, et al.
(författare)
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The DREAM Dataset : Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy
- 2020
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 15:8
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Tidskriftsartikel (refereegranskat)abstract
- We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.
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| 2. |
- Cai, Haibin, et al.
(författare)
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Sensing-enhanced Therapy System for Assessing Children with Autism Spectrum Disorders : A Feasibility Study
- 2019
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 19:4, s. 1508-1518
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Tidskriftsartikel (refereegranskat)abstract
- It is evident that recently reported robot-assisted therapy systems for assessment of children with autism spectrum disorder (ASD) lack autonomous interaction abilities and require significant human resources. This paper proposes a sensing system that automatically extracts and fuses sensory features such as body motion features, facial expressions, and gaze features, further assessing the children behaviours by mapping them to therapist-specified behavioural classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behaviour assessment. IEEE
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| 3. |
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| 4. |
- Esteban, Pablo G., et al.
(författare)
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How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder
- 2017
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Ingår i: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 8:1, s. 18-38
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Tidskriftsartikel (refereegranskat)abstract
- Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.
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| 5. |
- Skoglund, Alexander, 1976-
(författare)
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Programming by demonstration of robot manipulators
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- If a non-expert wants to program a robot manipulator he needs a natural interface that does not require rigorous robot programming skills. Programming-by-demonstration (PbD) is an approach which enables the user to program a robot by simply showing the robot how to perform a desired task. In this approach, the robot recognizes what task it should perform and learn how to perform it by imitating the teacher. One fundamental problem in imitation learning arises from the fact that embodied agents often have different morphologies. Thus, a direct skill transfer from human to a robot is not possible in the general case. Therefore, we need a systematic approach to PbD that takes the capabilities of the robot into account–regarding both perception and body structure. In addition, the robot should be able to learn from experience and improve over time. This raises the question of how to determine the demonstrator’s goal or intentions. We show that this is possible–to some degree–to infer from multiple demonstrations. We address the problem of generation of a reach-to-grasp motion that produces the same results as a human demonstration. It is also of interest to learn what parts of a demonstration provide important information about the task. The major contribution is the investigation of a next-state-planner using a fuzzy time-modeling approach to reproduce a human demonstration on a robot. We show that the proposed planner can generate executable robot trajectories based on a generalization of multiple human demonstrations. We use the notion of hand-states as a common motion language between the human and the robot. It allows the robot to interpret the human motions as its own, and it also synchronizes reaching with grasping. Other contributions include the model-free learning of human to robot mapping, and how an imitation metric ca be used for reinforcement learning of new robot skills. The experimental part of this thesis presents the implementation of PbD of pick-and-place-tasks on different robotic hands/grippers. The different platforms consist of manipulators and motion capturing devices.
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