Increasing compliance with wearing a medical device in children with autism

Health professionals often recommend the use of medical devices to assess the health, monitor the well-being, or improve the quality of life of their patients. Children with autism may present challenges in these situations as their sensory peculiarities may increase refusals to wear such devices. To address this issue, we systematically replicated prior research by examining the effects of differential reinforcement of other behavior (DRO) to increase compliance with wearing a heart rate monitor in 2 children with autism. The intervention increased compliance to 100% for both participants when an edible reinforcer was delivered every 90 s. The results indicate that DRO does not require the implementation of extinction to increase compliance with wearing a medical device. More research is needed to examine whether the reinforcement schedule can be further thinned

Prerequisites for Affective Signal Processing (ASP)

Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful Affective Signal Processing: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community. Using these directives, a critical analysis of a real-world case is provided. This illustrates that the prerequisites can become a valuable guide for Affective Signal Processing (ASP)

Differences in Visual Field Bias in Emotional Attribution Tasks Between Children with Autism Spectrum Disorders and Typical Development

Autism spectrum disorders (ASD) are characterized by social deficits in emotional comprehension. Since typical emotional attribution improves when using the left visual field, effects of lateralization on facial affect assessment were compared between children with ASD, pervasive developmental disorder not otherwise specified (PDD-NOS) and typical development (TD). The ASD group showed significantly lower percent accuracy, longer response time and slower pulse rate than the TD group. Within the ASD group, there was a significant right visual field bias in emotional attribution tasks, which contrasted with the left visual field bias seen within the TD group. The PDD-NOS group demonstrated no visual field advantage. Emotional attribution tasks could be an assessment tool to deferentially diagnose disorders within the autism spectrum

A Cluster Analysis of Challenging Behaviors in Autism Spectrum Disorder

We apply cluster analysis to a sample of 2,116 children with Autism Spectrum Disorder in order to identify patterns of challenging behaviors observed in home and centerbased clinical settings. The largest study of this type to date, and the first to employ machine learning, our results indicate that while the presence of multiple challenging behaviors is common, in most cases a dominant behavior emerges. Furthermore, the trend is also observed when we train our cluster models on the male and female samples separately. This work provides a basis for future studies to understand the relationship of challenging behavior profiles to learning outcomes, with the ultimate goal of providing personalized therapeutic interventions with maximum efficacy and minimum time and cost

Emotions Recognition in people with Autism using Facial Expressions and Machine Learning Techniques: Survey

في الآونة الأخيرة ، اهتمت الكثير من الدراسات بالتعرف على المشاعر واكتشافها لدى الأشخاص المصابين بالتوحد. الهدف الرئيسي من هذه الورقة هو مسح الدراسات المختلفة التي تتعلق بالحالة العاطفية للأشخاص المصابين بالتوحد. يتضمن الاستطلاع جزأين ، يركز الجزء الأول على الدراسات التي استخدمت تعابير الوجه للتعرف على المشاعر واكتشافها. حيث تعتبر تعبيرات الوجه من التقنيات العاطفية المهمة التي تستخدم للتعبير عن أنماط مختلفة من المشاعر. ركزت الأجزاء الثانية من هذه الدراسة على الأساليب التقنية المختلفة مثل التعلم الآلي والتعلم العميق والخوارزميات الأخرى التي تستخدم لتحليل وتحديد سلوكيات الوجه للأشخاص المصابين بالتوحد. للعثور على الحل الأمثل ، يتم من خلال التحقيق في مقارنة أنظمة الكشف عن المشاعر الحالية في هذه الورقة.Recently, a lot of studies have been interested in recognizing and detection of emotions in people with autism.  The main goal of this paper is to survey different studies which have been concerned emotional state of people with autism.  The survey includes two parts, first one focused on studies which use facial expressions to recognize and detect emotions. As facial expressions are considered the affective and important techniques which is used to express different patterns of emotions.  Second parts of this study, focuses on different technical methods like machine learning, deep learning and other algorithms that are employed to analyze and determine the facial behaviors of people with autism. To find the optimal solution, a comparison of current emotion-detecting systems is investigated in this paper

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Affective State Detection using fNIRs and Machine Learning

Affective states regulate our day to day to function and has a tremendous effect on mental and physical health. Detection of affective states is of utmost importance for mental health monitoring, smart entertainment selection and dynamic workload management. In this paper, we discussed relevant literature on affective state detection using physiology data, the benefits and limitations of different sensors and methods used for collecting physiology data, and our rationale for selecting functional near-infrared spectroscopy. We present the design of an experiment involving nine subjects to evoke the affective states of meditation, amusement and cognitive load and the results of the attempt to classify using machine learning. A mean accuracy of 83.04% was achieved in three class classification with an individual model; 84.39% accuracy was achieved for a group model and 60.57% accuracy was achieved for subject independent model using leave one out cross validation. It was found that prediction accuracy for cognitive load was higher (evoked using a pen and paper task) than the other two classes (evoked using computer bases tasks). To verify that this discrepancy was not due to motor skills involved in the pen and paper task, a second experiment was conducted using four participants and the results of that experiment has also been presented in the paper