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Kim W, Seong M, Kim KJ, Kim S. Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder. Sci Data 2024; 11:299. [PMID: 38491000 PMCID: PMC10942992 DOI: 10.1038/s41597-024-03132-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Engagement plays a key role in improving the cognitive and motor development of children with autism spectrum disorder (ASD). Sensing and recognizing their engagement is crucial before sustaining and improving the engagement. Engaging technologies involving interactive and multi-sensory stimuli have improved engagement and alleviated hyperactive and stereotyped behaviors. However, due to the scarcity of data on engagement recognition for children with ASD, limited access to and small pools of participants, and the prohibitive application requirements such as robots, high cost, and expertise, implementation in real world is challenging. However, serious games have the potential to overcome those drawbacks and are suitable for practical use in the field. This study proposes Engagnition, a dataset for engagement recognition of children with ASD (N = 57) using a serious game, "Defeat the Monster," based on enhancing recognition and classification skills. The dataset consists of physiological and behavioral responses, annotated by experts. For technical validation, we report the distributions of engagement and intervention, and the signal-to-noise ratio of physiological signals.
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Affiliation(s)
- Won Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Minwoo Seong
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Kyung-Joong Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - SeungJun Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea.
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Gómez-Espinosa A, Moreno JC, Pérez-de la Cruz S. Assisted Robots in Therapies for Children with Autism in Early Childhood. SENSORS (BASEL, SWITZERLAND) 2024; 24:1503. [PMID: 38475039 DOI: 10.3390/s24051503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/22/2024] [Accepted: 02/24/2024] [Indexed: 03/14/2024]
Abstract
Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). Currently, one way to address this problem is by using robotic systems to address the abilities that are altered in these children. The aim of this review will be to analyse the effectiveness of the incorporation of the different robotic systems currently existing in the treatment of children up to 10 years of age diagnosed with autism. A systematic review has been carried out in the PubMed, Scopus, Web of Science, and Dialnet databases, with the following descriptors: child, autism, and robot. The search yielded 578 papers, and nine were selected after the application of the PRISMA guideline. The quality of the studies was analysed with the PEDRo scale, and only those with a score between four and six were selected. From this study, the conclusion is that the use of robots, in general, improves children's behaviour in the short term, but longer-term experiences are necessary to achieve more conclusive results.
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Affiliation(s)
- Ana Gómez-Espinosa
- Department of Informatics, University of Almería, ceiA3, CIESOL, 04120 Almería, Spain
| | - José Carlos Moreno
- Department of Informatics, University of Almería, ceiA3, CIESOL, 04120 Almería, Spain
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Pandya S, Jain S, Verma J. A comprehensive analysis towards exploring the promises of AI-related approaches in autism research. Comput Biol Med 2024; 168:107801. [PMID: 38064848 DOI: 10.1016/j.compbiomed.2023.107801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents challenges in communication, social interaction, repetitive behaviour, and limited interests. Detecting ASD at an early stage is crucial for timely interventions and an improved quality of life. In recent times, Artificial Intelligence (AI) has been increasingly used in ASD research. The rise in ASD diagnoses is due to the growing number of ASD cases and the recognition of the importance of early detection, which leads to better symptom management. This study explores the potential of AI in identifying early indicators of autism, aligning with the United Nations Sustainable Development Goals (SDGs) of Good Health and Well-being (Goal 3) and Peace, Justice, and Strong Institutions (Goal 16). The paper aims to provide a comprehensive overview of the current state-of-the-art AI-based autism classification by reviewing recent publications from the last decade. It covers various modalities such as Eye gaze, Facial Expression, Motor skill, MRI/fMRI, and EEG, and multi-modal approaches primarily grouped into behavioural and biological markers. The paper presents a timeline spanning from the history of ASD to recent developments in the field of AI. Additionally, the paper provides a category-wise detailed analysis of the AI-based application in ASD with a diagrammatic summarization to convey a holistic summary of different modalities. It also reports on the successes and challenges of applying AI for ASD detection while providing publicly available datasets. The paper paves the way for future scope and directions, providing a complete and systematic overview for researchers in the field of ASD.
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Affiliation(s)
- Shivani Pandya
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Swati Jain
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Jaiprakash Verma
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
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Twala B, Molloy E. On effectively predicting autism spectrum disorder therapy using an ensemble of classifiers. Sci Rep 2023; 13:19957. [PMID: 37968315 PMCID: PMC10651853 DOI: 10.1038/s41598-023-46379-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 10/31/2023] [Indexed: 11/17/2023] Open
Abstract
An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children.
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Affiliation(s)
- Bhekisipho Twala
- Office of the Deputy Vice-Chancellor (Digital Transformation), Tshwane University of Technology, Private Bag x680, Pretoria, 001, South Africa.
| | - Eamon Molloy
- Waterford Institute of Technology, School of Science & Computing, Waterford, Ireland
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Dataset on Force Myography for Human–Robot Interactions. DATA 2022. [DOI: 10.3390/data7110154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.
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Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review. Cognit Comput 2022. [DOI: 10.1007/s12559-021-09940-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Szymona B, Maciejewski M, Karpiński R, Jonak K, Radzikowska-Büchner E, Niderla K, Prokopiak A. Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research. SENSORS (BASEL, SWITZERLAND) 2021; 21:3720. [PMID: 34071829 PMCID: PMC8198717 DOI: 10.3390/s21113720] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/24/2021] [Accepted: 05/26/2021] [Indexed: 01/16/2023]
Abstract
Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that it is possible to perform some therapy using a robot. This article is a synthesis review of the literature on research with the use of robots in the therapy of children with the diagnosis of early childhood autism. The review includes scientific journals from 2005-2021. Using descriptors: ASD (Autism Spectrum Disorders), Social robots, and Robot-based interventions, an analysis of available research in PubMed, Scopus and Web of Science was done. The results showed that a robot seems to be a great tool that encourages contact and involvement in joint activities. The review of the literature indicates the potential value of the use of robots in the therapy of people with autism as a facilitator in social contacts. Robot-Assisted Autism Therapy (RAAT) can encourage child to talk or do exercises. In the second aspect (prompting during a conversation), a robot encourages eye contact and suggests possible answers, e.g., during free conversation with a peer. In the third aspect (teaching, entertainment), the robot could play with autistic children in games supporting the development of joint attention. These types of games stimulate the development of motor skills and orientation in the body schema. In future work, a validation test would be desirable to check whether children with ASD are able to do the same with a real person by learning distrust and cheating the robot.
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Affiliation(s)
- Barbara Szymona
- Sanus Medical Center, Day Treatment Center for Children with Autism, Magnoliowa 2, 20-143 Lublin, Poland;
| | - Marcin Maciejewski
- Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
| | - Robert Karpiński
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
- Chair and I Department of Psychiatry, Psychotherapy, and Early Intervention, Medical University of Lublin, 20-439 Lublin, Poland
| | - Kamil Jonak
- Department of Clinical Neuropsychiatry, Medical University of Lublin, 20-439 Lublin, Poland;
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, 20-618 Lublin, Poland
| | - Elżbieta Radzikowska-Büchner
- Department of Plastic, Reconstructive and Maxillary Surgery, Central Clinical Hospital MSWiA, Wołoska 137, 02-507 Warsaw, Poland;
| | - Konrad Niderla
- Dream-Art sp. z o.o., Capital Park, Rejtana 67/5.16, 35-326 Rzeszów, Poland;
- University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland
| | - Anna Prokopiak
- Alpha Medical Center, Warszawska 15, 20-803 Lublin, Poland;
- Department of Special Psychopedagogy and Special Sociopedagogy, Maria Curie-Sklodowska University in Lublin, Curie-Skłodowskiej 5, 20-031 Lublin, Poland
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