| 1. |
- Bartlett, Madeleine E., et al.
(författare)
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Requirements for Robotic Interpretation of Social Signals “in the Wild” : Insights from Diagnostic Criteria of Autism Spectrum Disorder
- 2020
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Ingår i: Information. - : MDPI. - 2078-2489. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to charact
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| 2. |
- Bartlett, Madeleine, et al.
(författare)
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What Can You See? : Identifying Cues on Internal States From the Movements of Natural Social Interactions
- 2019
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Research Foundation. - 2296-9144. ; 6:49
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the field of Human-Robot Interaction (HRI) has seen an increasingdemand for technologies that can recognize and adapt to human behaviors and internalstates (e.g., emotions and intentions). Psychological research suggests that humanmovements are important for inferring internal states. There is, however, a need to betterunderstand what kind of information can be extracted from movement data, particularlyin unconstrained, natural interactions. The present study examines which internal statesand social constructs humans identify from movement in naturalistic social interactions.Participants either viewed clips of the full scene or processed versions of it displaying2D positional data. Then, they were asked to fill out questionnaires assessing their socialperception of the viewed material. We analyzed whether the full scene clips were moreinformative than the 2D positional data clips. First, we calculated the inter-rater agreementbetween participants in both conditions. Then, we employed machine learning classifiersto predict the internal states of the individuals in the videos based on the ratingsobtained. Although we found a higher inter-rater agreement for full scenes comparedto positional data, the level of agreement in the latter case was still above chance,thus demonstrating that the internal states and social constructs under study wereidentifiable in both conditions. A factor analysis run on participants’ responses showedthat participants identified the constructs interaction imbalance, interaction valence andengagement regardless of video condition. The machine learning classifiers achieveda similar performance in both conditions, again supporting the idea that movementalone carries relevant information. Overall, our results suggest it is reasonable to expecta machine learning algorithm, and consequently a robot, to successfully decode andclassify a range of internal states and social constructs using low-dimensional data (suchas the movements and poses of observed individuals) as input.
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| 3. |
- 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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| 4. |
- 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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| 5. |
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| 6. |
- Chersi, Fabian, et al.
(författare)
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Sentence processing : linking language to motor chains
- 2010
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Ingår i: Frontiers in Neurorobotics. - : Frontiers Media S.A.. - 1662-5218. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- A growing body of evidence in cognitive science and neuroscience points towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language is grounded in the sensorimotor system and language understanding is based on a mental simulation process (Jeannerod, 2007; Gallese, 2008; Barsalou, 2009). This means that during action words and sentence comprehension the same perception, action, and emotion mechanisms implied during interaction with objects are recruited. Among the neural underpinnings of this simulation process an important role is played by a sensorimotor matching system known as the mirror neuron system (Rizzolatti and Craighero, 2004). Despite a growing number of studies, the precise dynamics underlying the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations’ internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools.
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| 7. |
- Da Lio, Mauro, et al.
(författare)
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Exploiting Dream-Like Simulation Mechanisms to Develop Safer Agents for Automated Driving The "Dreams4Cars" EU Research and Innovation Action
- 2017
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Ingår i: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9781538615263 - 9781538615270
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Konferensbidrag (refereegranskat)abstract
- Automated driving needs unprecedented levels of reliably and safety before marked deployment. The average human driver fatal accident rate is 1 every 100 million miles. Automated vehicles will have to provably best these figures. This paper introduces the notion of dream-like mechanisms as a simulation technology to produce a large number of hypothetical design and test scenarios - especially focusing on variations of more frequent dangerous and near miss events. Grounded in the simulation hypothesis of cognition, we show here some principles for effective simulation mechanisms and an artificial cognitive system architecture that can learn from the simulated situations.
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| 8. |
- Drejing, Karl, 1988-, et al.
(författare)
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Engagement: A traceable motivational concept in human-robot interaction
- 2015
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Ingår i: Affective Computing and Intelligent Interaction (ACII), 2015 International Conference on. - : IEEE Computer Society. - 9781479999538 ; , s. 956-961
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Konferensbidrag (refereegranskat)abstract
- Engagement is essential to meaningful social interaction between humans. Understanding the mechanisms by which we detect engagement of other humans can help us understand how we can build robots that interact socially with humans. However, there is currently a lack of measurable engagement constructs on which to build an artificial system that can reliably support social interaction between humans and robots. This paper proposes a definition, based on motivation theories, and outlines a framework to explore the idea that engagement can be seen as specific behaviors and their attached magnitude or intensity. This is done by the use of data from multiple sources such as observer ratings, kinematic data, audio and outcomes of interactions. We use the domain of human-robot interaction in order to illustrate the application of this approach. The framework further suggests a method to gather and aggregate this data. If certain behaviors and their attached intensities co-occur with various levels of judged engagement, then engagement could be assessed by this framework consequently making it accessible to a robotic platform. This framework could improve the social capabilities of interactive agents by adding the ability to notice when and why an agent becomes disengaged, thereby providing the interactive agent with an ability to reengage him or her. We illustrate and propose validation of our framework with an example from robot-assisted therapy for children with autism spectrum disorder. The framework also represents a general approach that can be applied to other social interactive settings between humans and robots, such as interactions with elderly people.
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| 9. |
- Durán, Boris, et al.
(författare)
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Modelling interaction in multi-modal affordance processing with neural dynamics
- 2012
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Ingår i: From Animals to Animats 12. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642330926 - 9783642330933 ; , s. 75-84
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Konferensbidrag (refereegranskat)abstract
- Behavioral studies on the activation of affordances by understanding observation and action sentences on graspable objects show a direct relationship between the canonical orientation of graspable objects, their dimension and the kind of grip required by those objects to be grasped. The present work introduces the concepts of Dynamic Field Theory for modeling the results observed in the behavioral studies previously mentioned. The model was not only able to replicate qualitatively similar results regarding reaction times, but also the identification of same versus different object and the distinction between observable versus action sentences. The model shows the potential of dynamic field theory for the design and implementation of brain inspired cognitive systems. © 2012 Springer-Verlag.
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| 10. |
- 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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