Deep recurrent Q-learning of behavioral intervention delivery by a robot from demonstration data

Clark-Turner, Madison; Begum, Momotaz

doi:10.1109/roman.2017.8172429

Cited by 11 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clark-Turner and Begum (2017) provide a framework based on the Learning from Demonstration (LfD). In this work, the goal is for software (of the robot) to learn to perform a behavioral intervention according to what it learns by observing a human applying the intervention.…”

Section: Discussionmentioning

confidence: 99%

A review of the use of computational technology in applied behavior analysis

et al. 2019

View full text Add to dashboard Cite

Applied behavior analysis (ABA) is a behavioral science that aims to teach specific and socially relevant behaviors to people with different repertoires. Technology can aid behavior analysis interventions supporting different educational agents (e.g. psychologists, parents, and teachers) as well as patients. In this article, we reviewed how researchers are using technology in ABA interventions with children with autism spectrum disorder (ASD). We present the results obtained from a systematic review of the literature. The purpose was to map the employment of the computational technology in ABA interventions, as well as to discuss the primary technologies used, their benefits, and their limitations. Based on our review, we noticed that technology-based interventions are in the early stages of development. We evidenced it by the methodological limitations in many of the studies we found, and the relatively simplistic nature of many of the technological applications (e.g. inability to customize the software to meet individual learner needs). We also provide suggestions, based on these findings, for how researchers may advance the studies in this area to improve the lives of children with ASD and their families.

show abstract

Section: Discussionmentioning

confidence: 99%

A review of the use of computational technology in applied behavior analysis

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Therefore, it was not surprising that many papers in our past reviews ( 6 ) related to proposing algorithms to assess ADHD using different machine learning approaches to classify brain activity ( 27 – 40 ). Similarly, papers often contributed to the scholarly discourse of developing novel prototypes ( 41 – 44 ) in which the efficacy had not been demonstrated. Sometimes with empirical evidence about usability, the prototype itself is considered a contribution in the HCI field ( 45 ), including protocols for data privacy and analysis of data gathered from input devices.…”

Section: Methodsmentioning

confidence: 99%

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Cibrian

Monteiro

Schuck

et al. 2022

Front. Digit. Health

View full text Add to dashboard Cite

“…In the study [110], learning from a demonstration framework (LfD) is presented that uses a deep recurrent Qnetwork (DRQN) to learn how to execute a behavioural intervention (BI) based on demonstrations by a human. BIs are highly structured procedures in which children with ADHD learn new behaviors and life skills.…”

Section: Intervention Of Educational Therapymentioning

confidence: 99%

Robotic Technologies in ADHD Care: Literature Review

et al. 2022

View full text Add to dashboard Cite

Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords 'robotics' and several NNDs such as 'ADHD', 'Autism Spectrum Disorder (ASD)', 'cerebral palsy', and 'dementia' in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

show abstract

Deep recurrent Q-learning of behavioral intervention delivery by a robot from demonstration data

Cited by 11 publications

References 16 publications

A review of the use of computational technology in applied behavior analysis

A review of the use of computational technology in applied behavior analysis

Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD

Robotic Technologies in ADHD Care: Literature Review

Contact Info

Product

Resources

About