Vision-Based Activity Recognition in Children with Autism-Related Behaviors [PDF]
Heliyon, Volume 9, Issue 6, June 2023, e16763, 2022 Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early
arxiv +1 more source
Detection of developmental language disorder in Cypriot Greek children using a neural network algorithm [PDF]
Journal of Technology in Behavioral Science, 2024, 2023 Children with developmental language disorder (DLD) encounter difficulties in acquiring various language structures. Early identification and intervention are crucial to prevent negative long-term outcomes impacting the academic, social, and emotional development of children.
arxiv +1 more source
Application of Machine Learning in Identification of Best Teaching Method for Children with Autism Spectrum Disorder [PDF]
arXiv, 2023 A good teaching method is incomprehensible for an autistic child. The autism spectrum disorder is a very diverse phenomenon. It is said that no two autistic children are the same. So, something that works for one child may not be fit for another. The same case is true for their education. Different children need to be approached with different teaching
arxiv
Development of an autism screening classification model for toddlers [PDF]
, 2021 Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. Getting a clear diagnosis for a child is necessary for starting early intervention and having access to therapy services.
arxiv +1 more source
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems [PDF]
arXiv, 2022 We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games.
arxiv
The mentor-child paradigm for individuals with autism spectrum disorders [PDF]
arXiv, 2023 Our aim is to analyze the relevance of the mentor-child paradigm with a robot for individuals with Autism Spectrum Disorders, and the adaptations required. This method could allow a more reliable evaluation of the socio-cognitive abilities of individuals with autism, which may have been underestimated due to pragmatic factors.
arxiv
Enumeration of $d$-combining Tree-Child Networks [PDF]
arXiv, 2022 Tree-child networks are one of the most prominent network classes for modeling evolutionary processes which contain reticulation events. Several recent studies have addressed counting questions for {\it bicombining tree-child networks} which are tree-child networks with every reticulation node having exactly two parents.
arxiv
Learning Domain Invariant Representations for Child-Adult Classification from Speech [PDF]
arXiv, 2019 Diagnostic procedures for ASD (autism spectrum disorder) involve semi-naturalistic interactions between the child and a clinician. Computational methods to analyze these sessions require an end-to-end speech and language processing pipeline that go from raw audio to clinically-meaningful behavioral features.
arxiv
Project Rosetta: A Childhood Social, Emotional, and Behavioral Developmental Ontology [PDF]
arXiv, 2018 There is a wide array of existing instruments used to assess childhood behavior and development for the evaluation of social, emotional and behavioral disorders. Many of these instruments either focus on one diagnostic category or encompass a broad set of childhood behaviors.
arxiv
Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry [PDF]
arXiv, 2022 Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for ...
arxiv