The Central Nervous System of humans continuously evolves when children engage in new activities. These activities are progressing from learning to eat as a baby over playing during childhood up to homework in all its dimensions. These activities, which are meaningful and relevant to everyone, constitute what occupational therapists call "occupations". The successful execution of these occupations makes the development of new roles as well as the construction of a correct state of health possible. Due to these reasons, occupational performance is a fundamental part of development throughout childhood. Homework is complex and extends beyond the school context to the home. For many children, the performance of these homework items is a difficult challenge or even impossible to overcome without the help of an adult. This article presents the design, implementation, and functional validation of an intelligent home environment that uses homework activities as a support tool for children with or without attention disabilities. In this project, the Internet of Things (IoT) paradigm is combined with the development of robotic assistance to implement an intelligent home environment. In this environment, we have included intelligent things (where the usual study chairs and desks become smart objects) that determine in real-time the child's behavior during the development of homework and a robotic assistant which interacts with the children providing the necessary accompaniment (supervision and guidelines) just as a therapist would do. This development has been functionally validated by tests on several school-aged children without pathologies. In a later phase of the study, the proposal will be validated with children with different pathologies with an impact on learning, including Attention Disorder Hyperactivity Disorder (ADHD). The goal is the generation of intelligent places for therapeutic purposes within the home as assistance for children having difficulties to work with their homework assignment due to ADHD.
The importance of an early rehabilitation process in children with cerebral palsy (CP) is widely recognized. On the one hand, new and useful treatment tools such as rehabilitation systems based on interactive technologies have appeared for rehabilitation of gross motor movements. On the other hand, from the therapeutic point of view, performing rehabilitation exercises with the facial muscles can improve the swallowing process, the facial expression through the management of muscles in the face, and even the speech of children with cerebral palsy. However, it is difficult to find interactive games to improve the detection and evaluation of oral-facial musculature dysfunctions in children with CP. This paper describes a framework based on strategies developed for interactive serious games that is created both for typically developed children and children with disabilities. Four interactive games are the core of a Virtual Environment called SONRIE. This paper demonstrates the benefits of SONRIE to monitor children’s oral-facial difficulties. The next steps will focus on the validation of SONRIE to carry out the rehabilitation process of oral-facial musculature in children with cerebral palsy.
Smart spaces foster the development of natural and appropriate forms of human-computer interaction by taking advantage of home customization. The interaction potential of the Smart Home, which is a special type of smart space, is of particular interest in fields in which the acceptance of new technologies is limited and restrictive. The integration of smart home design patterns with sensitive solutions can increase user acceptance. In this paper, we present the main challenges that have been identified in the literature for the successful deployment of sensitive services (e.g., telemedicine and assistive services) in smart spaces and a software architecture that models the functionalities of a Smart Home platform that are required to maintain and support such sensitive services. This architecture emphasizes user interaction as a key concept to facilitate the acceptance of sensitive services by end-users and utilizes activity theory to support its innovative design. The application of activity theory to the architecture eases the handling of novel concepts, such as understanding of the system by patients at home or the affordability of assistive services. Finally, we provide a proof-of-concept implementation of the architecture and compare the results with other architectures from the literature.
EDUCERE (Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders) is a government funded research and development project. EDUCERE objectives are to investigate, develop, and evaluate innovative solutions for society to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. In the EDUCERE project, an ethical impact assessment is carried out linked to a minors' data protection rights. Using a specific methodology, the project has achieved some promising results. These include use of a prototype of smart toys to detect development difficulties in children. In addition, privacy protection measures which take into account the security concerns of health data, have been proposed and applied. This latter security framework could be useful in other Internet of Things related projects. It consists of legal and technical measures. Special attention has been placed in the transformation of bulk data such as acceleration and jitter of toys into health data when patterns of atypical development are found. The article describes the different security profiles in which users are classified.
Neuro-evolutive development from birth until the age of six years is a decisive factor in a child's quality of life. Early detection of development disorders in early childhood can facilitate necessary diagnosis and/or treatment. Primary-care pediatricians play a key role in its detection as they can undertake the preventive and therapeutic actions requested to promote a child's optimal development. However, the lack of time and little specific knowledge at primary-care avoid to applying continuous early-detection anomalies procedures. This research paper focuses on the deployment and evaluation of a smart system that enhances the screening of language disorders in primary care. Pediatricians get support to proceed with early referral of language disorders. The proposed model provides them with a decision-support tool for referral actions to trigger essential diagnostic and/or therapeutic actions for a comprehensive individual development. The research was conducted by starting from a sample of 60 cases of children with language disorders. Validation was carried out through two complementary steps: first, by including a team of seven experts from the fields of neonatology, pediatrics, neurology and language therapy, and, second, through the evaluation of 21 more previously diagnosed cases. The results obtained show that therapist positively accepted the system proposal in 18 cases (86%) and suggested system redesign for single referral to a speech therapist in three remaining cases.
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.
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