A5038954067- Autism Spectrum Disorder Research
- Child Development and Digital Technology
- Attention Deficit Hyperactivity Disorder
- Virology and Viral Diseases
- Cerebral Palsy and Movement Disorders
- Infant Health and Development
- Natural Language Processing Techniques
- Speech and dialogue systems
- Topic Modeling
- Genetics and Neurodevelopmental Disorders
- Gaze Tracking and Assistive Technology
- Face and Expression Recognition
- Memory and Neural Mechanisms
- Technology Use by Older Adults
- Face recognition and analysis
- Child Nutrition and Feeding Issues
- Memory Processes and Influences
- Video Surveillance and Tracking Methods
- Neural and Behavioral Psychology Studies
- Family and Disability Support Research
- Digital Mental Health Interventions
Duke University
2015-2020
RTX (United States)
2020
Center for Autism and Related Disorders
2018-2020
Duke University Hospital
2020
Duke Medical Center
2020
Marcus (United States)
2019
To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered four 16-31 months old (mean = 22) participated this study. Twenty-two had disorder 82 typical development or developmental delay. Toddlers watched video stimuli on a tablet while built-in camera recorded their head movement. Computer measured participants' response name calls. Reliability algorithm was tested against human...
Current tools for objectively measuring young children's observed behaviors are expensive, time-consuming, and require extensive training professional administration. The lack of scalable, reliable, validated impacts access to evidence-based knowledge limits our capacity collect population-level data in non-clinical settings. To address this gap, we developed mobile technology videos children while they watched movies designed elicit autism-related then used automatic behavioral coding these...
Evidence suggests that differences in motor function are an early feature of autism spectrum disorder (ASD). One aspect ability develops during childhood is postural control, reflected the to maintain a steady head and body position without excessive sway. Observational studies have documented control older children with ASD. The present study used computer vision analysis assess midline as rate spontaneous movements states active attention, 104 toddlers between 16-31 months age (Mean = 22...
Observational behavior analysis plays a key role for the discovery and evaluation of risk markers many neurodevelopmental disorders. Research on autism spectrum disorder (ASD) suggests that behavioral can be observed at 12 months age or earlier, with diagnosis possible 18 months. To date, these studies evaluations involving observational tend to rely heavily clinical practitioners specialists who have undergone intensive training able reliably administer carefully designed...
Autism spectrum disorder (ASD) is associated with deficits in the processing of social information and difficulties interaction, individuals ASD exhibit atypical attention gaze. Traditionally, gaze studies have relied upon precise constrained means monitoring using expensive equipment laboratories. In this work we develop a low-cost off-the-shelf alternative for measuring that can be used natural settings. The head iris positions 104 16-31 months children, an age range appropriate screening...
In spite of recent advances in the genetics and neuroscience early childhood mental health, behavioral observation is still gold standard screening, diagnosis, outcome assessment. Unfortunately, clinical often subjective, needs significant rater training, does not captur
Abstract To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. that end, a previous study demonstrated tablet‐based application (app) assessed several risk behaviors distinguished between toddlers with ASD non‐ASD toddlers. Using vocal data collected during this study, investigated whether vocalizations uttered administration app can distinguish among aged 16–31 months typical development (TD), language or developmental delay...
Volitional exploration and learning are key to adaptive behavior, yet their characterization remains a complex problem for cognitive science. Exploration has been posited as mechanism by which motivation promotes memory, but this relationship is not well-understood, in part because novel stimuli that motivate also reliably elicit changes neuromodulatory brain systems directly alter memory formation, via effects on neural plasticity. To deconfound interrelationships between motivation,...
Dialog State Tracking (DST) is a problem space in which the effective vocabulary practically limitless. For example, domain of possible movie titles or restaurant names bound only by limits language. As such, DST systems often encounter out-of-vocabulary words at inference time that were never encountered during training. To combat this issue, we present targeted data augmentation process, practitioner observes types errors made on held-out evaluation data, and then modifies training with...
Deep Neural Networks (DNNs) that achieve state-of-the-art results are still prone to suffer performance degradation when deployed in many real-world scenarios due shifts between the training and deployment domains. Limited data from a given setting can be enriched through synthesis, then used calibrate pre-trained DNN improve setting. Most enrichment approaches try generate as much possible; however, this blind approach is computationally expensive lead generating redundant data. Contrary...