Stigma related to physical and sensory disabilities.

2013 ◽  
pp. 93-120 ◽  
Author(s):  
Hanoch Livneh ◽  
Fong Chan ◽  
Cahit Kaya
Keyword(s):  
Sensory Disabilities

Bus Accessibility for People with Sensory Disabilities

1999 ◽  
Vol 1671 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Katharine Hunter-Zaworski ◽  
M. L. Hron
Keyword(s):  
Sensory Disabilities

Guidelines to address barriers in clinical training for trainees with sensory disabilities.

2021 ◽  
Author(s):  
Jennifer G. Pearlstein ◽  
Adam T. Schmidt ◽  
Emily M. Lund ◽  
Lauren R. Khazem ◽  
Nancy H. Liu
Keyword(s):  
Clinical Training ◽  
Sensory Disabilities

Haptic-assistive technologies for audition and vision sensory disabilities

2017 ◽  
Vol 13 (4) ◽  
pp. 394-421 ◽  
Author(s):  
Francesca Sorgini ◽  
Renato Caliò ◽  
Maria Chiara Carrozza ◽  
Calogero Maria Oddo
Keyword(s):  
Assistive Technologies ◽  
Sensory Disabilities

scholarly journals Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Jesús Leonardo López-Hernández ◽  
Israel González-Carrasco ◽  
José Luis López-Cuadrado ◽  
Belén Ruiz-Mezcua
Keyword(s):  
Machine Learning ◽  
Positive Emotions ◽  
Machine Learning Techniques ◽  
Visual Disabilities ◽  
Brain Signals ◽  
Learning Techniques ◽  
Sensory Disabilities ◽  
Recognition Of Emotions

Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

scholarly journals Variations in patterns of care across neonatal units and their associations with outcomes in very preterm infants: the French EPIPAGE-2 cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e035075
Author(s):  
Veronique Pierrat ◽  
Antoine Burguet ◽  
Laetitia Marchand-Martin ◽  
Gilles Cambonie ◽  
Anaëlle Coquelin ◽  
...  
Keyword(s):  
Cohort Study ◽  
Neonatal Intensive Care ◽  
Ductus Arteriosus ◽  
Hierarchical Cluster ◽  
Patterns Of Care ◽  
Very Preterm Infants ◽  
Very Preterm ◽  
Sensory Disabilities ◽  
Care Practices ◽  
Cluster 2

ObjectivesTo describe patterns of care for very preterm (VP) babies across neonatal intensive care units (NICUs) and associations with outcomes.DesignProspective cohort study, EPIPAGE-2.SettingFrance, 2011.Participants53 (NICUs); 2135 VP neonates born at 27 to 31 weeks.Outcome measuresClusters of units, defined by the association of practices in five neonatal care domains – respiratory, cardiovascular, nutrition, pain management and neurodevelopmental care. Mortality at 2 years corrected age (CA) or severe/moderate neuro-motor or sensory disabilities and proportion of children with scores below threshold on the neurodevelopmental Ages and Stages Questionnaire (ASQ).MethodsHierarchical cluster analysis to identify clusters of units. Comparison of outcomes between clusters, after adjustment for potential cofounders.ResultsThree clusters were identified: Cluster 1 with higher proportions of neonates free of mechanical ventilation at 24 hours of life, receiving early enteral feeding, and neurodevelopmental care practices (26 units; n=1118 babies); Cluster 2 with higher levels of patent ductus arteriosus and pain screening (11 units; n=398 babies); Cluster 3 with higher use of respiratory, cardiovascular and pain treatments (16 units; n=619 babies). No difference was observed between clusters for the baseline maternal and babies’ characteristics. No differences in outcomes were observed between Clusters 1 and 3. Compared with Cluster 1, mortality at 2 years CA or severe/moderate neuro-motor or sensory disabilities was lower in Cluster 2 (adjusted OR 0.46, 95% CI 0.25 to 0.84) but with higher proportion of children with an ASQ below threshold (adjusted OR 1.49, 95% CI 1.07 to 2.08).ConclusionIn French NICUs, care practices for VP babies were non-randomly associated. Differences between clusters were poorly explained by unit or population differences, but were associated with mortality and development at 2 years. Better understanding these variations may help to improve outcomes for VPT babies, as it is likely that some of these discrepancies are unwarranted.

Teaching Children with Dual Sensory Disabilities in Zimbabwe

1995 ◽  
Vol 89 (3) ◽  
pp. 301-306
Author(s):  
E.G. Wolf-Schein ◽  
N. Khan ◽  
M.E. Barrett ◽  
J.D. Schein
Keyword(s):  
Sensory Disabilities
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