Sensing Technologies for Autism Spectrum Disorder Screening and Intervention

People with Autism Spectrum Disorder Could Interact More Easily with a Robot than with a Human: Reasons and Limits

Individuals with Autism Spectrum Disorder show deficits in communication and social interaction, as well as repetitive behaviors and restricted interests. Interacting with robots could bring benefits to this population, notably by fostering communication and social interaction. Studies even suggest that people with Autism Spectrum Disorder could interact more easily with a robot partner rather than a human partner. We will be looking at the benefits of robots and the reasons put forward to explain these results. The interest regarding robots would mainly be due to three of their characteristics: they can act as motivational tools, and they are simplified agents whose behavior is more predictable than that of a human. Nevertheless, there are still many challenges to be met in specifying the optimum conditions for using robots with individuals with Autism Spectrum Disorder.

A Scoping Review of Studies on Assistive Technology Interventions and Their Impact on Individuals with Autism Spectrum Disorder in Arab Countries

The rising prevalence of autism spectrum disorder (ASD) in Arab countries necessitates evidence-based interventions. Assistive technology (AT) presents a promising approach. However, data on the pervasiveness of AT use and its effectiveness for individuals with ASD, specifically within Arab countries, remain scarce.

OBJECTIVE

To review the current literature on the AT interventions and outcomes reported for individuals with ASD in Arab countries.

METHODS

A scoping review adhering to PRISMA guidelines was undertaken to explore the utilization of AT, segmented into three categories: low-technology (low-tech), mid-technology (mid-tech), and high-technology (high-tech) devices.

RESULTS

Twelve studies had a pooled sample of 1547 participants, primarily male school-aged children with ASD. The AT applications evaluated ranged from low-tech visual schedules and support to high-tech virtual reality systems. Studies have reported the potential benefits of AT in improving communication, social, academic, adaptive, and functional abilities; however, comparative evidence between AT interventions is limited. The identified barriers to the adoption of AT included caregiver uncertainty about the use of AT and a lack of awareness of AT among professionals and the Arab community in general.

CONCLUSION

Available studies suggest that the adoption of AT can enhance the skills of individuals with ASD in Arab countries. However, more rigorous studies across diverse demographic groups and Arab national regions are needed to strengthen the evidence base and provide appropriate recommendations.

Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots.

A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum Disorders

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children’s states and risky environmental factors. Sensory management strategies were recommended to help improve children’s attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children’s performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children’s environment.

Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings

Aggression in children is frequent during the early years of childhood. Among children with psychiatric disorders in general, and autism in particular, challenging behaviours and aggression rates are higher. These can take on different forms, such as hitting, kicking, and throwing objects. Social robots that are able to detect undesirable interactions within its surroundings can be used to target such behaviours. In this study, we evaluate the performance of five machine learning techniques in characterizing five possible undesired interactions between a child and a social robot. We examine the effects of adding different combinations of raw data and extracted features acquired from two sensors on the performance and speed of prediction. Additionally, we evaluate the performance of the best developed model with children. Machine learning algorithms experiments showed that XGBoost achieved the best performance across all metrics (e.g., accuracy of 90%) and provided fast predictions (i.e., 0.004 s) for the test samples. Experiments with features showed that acceleration data were the most contributing factor on the prediction compared to gyroscope data and that combined data of raw and extracted features provided a better overall performance. Testing the best model with data acquired from children performing interactions with toys produced a promising performance for the shake and throw behaviours. The findings of this work can be used by social robot developers to address undesirable interactions in their robotic designs.

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

The Digital Divide in Technologies for Autism: Feasibility Considerations for Low- and Middle-Income Countries

Digital technologies have the potential to empower individuals with autism and their families. The COVID-19 pandemic emphasized and accelerated the drive towards technology for information, communication, training, clinical care and research, also in the autism community. However, 95% of individuals with autism live in low- and middle-income countries (LMIC) where access to electricity, internet and the ever-increasing range of digital devices may be highly limited. The World Bank coined the term ‘the digital divide’ to describe the disparities in access to digital technologies between high-income and LMIC contexts. Here we evaluated the feasibility of six emerging technologies for autism spectrum disorders, and reflected on key considerations for implementation in LMIC contexts to ensure that we do not inadvertently widen the pre-existing digital divide.

Sleep Dysregulation and Daytime Electrodermal Patterns in Children With Autism: A Descriptive Study

Sleep deficiency influences emotion and behavior regulation but the mechanisms of influence are poorly understood. Emotion, behavioral, and sleep theories highlight differences in autonomic function as a potential pathway of influence and research in typical populations draw links between sleep deficiency and autonomic dysregulation (e.g., elevated reactivity within the sympathetic nervous system). In populations at elevated risk for sleep deficiency/problems (i.e., individuals with autism), greater variability in sleep and autonomic/arousal profiles may be particularly informative. Using electrodermal activity (EDA) as an indicator of sympathetic nervous system activation, this descriptive pilot study aimed to document daytime EDA patterns in children with autism and to explore their relations with sleep dysregulation/deficiency. EDA and sleep were measured using ankle and wrist worn sensors in 13 children (Mean_age 6.11 years). EDA indices included nonspecific skin conductance responses (NSSCR) and tonic skin conductance levels (SCL). Descriptively, children in the dysregulated sleep group had fewer NSSCRs and lower SCL in the afternoon. This blunted physiological arousal profile/pattern is consistent with previous research, but this is the first study to explore how sleep may be linked. Notably, this pattern may not reflect sleep but an overall dysregulation profile which in this sample included: dysregulated sleep, a blunted afternoon arousal profile, and elevated ASD symptom severity. Replication with larger, more diverse samples is needed to disentangle the complex relations among sleep, arousal, and ASD behavioral features. However, this study represents an important first step in documenting extended daytime arousal patterns.

Understanding the experiences of self-injurious behavior in autism spectrum disorder: Implications for monitoring technology design

OBJECTIVE

Monitoring technology may assist in managing self-injurious behavior (SIB), a pervasive concern in autism spectrum disorder (ASD). Affiliated stakeholder perspectives should be considered to design effective and accepted SIB monitoring methods. We examined caregiver experiences to generate design guidance for SIB monitoring technology.

MATERIALS AND METHODS

Twenty-three educators and 16 parents of individuals with ASD and SIB completed interviews or focus groups to discuss needs related to monitoring SIB and associated technology use.

RESULTS

Qualitative content analysis of participant responses revealed 7 main themes associated with SIB and technology: triggers, emotional responses, SIB characteristics, management approaches, caregiver impact, child/student impact, and sensory/technology preferences.

DISCUSSION

The derived themes indicated areas of emphasis for design at the intersection of monitoring and SIB. Systems design at this intersection should consider the range of manifestations of and management approaches for SIB. It should also attend to interactions among children with SIB, their caregivers, and the technology. Design should prioritize the transferability of physical technology and behavioral data as well as the safety, durability, and sensory implications of technology.

CONCLUSIONS

The collected stakeholder perspectives provide preliminary groundwork for an SIB monitoring system responsive to needs as articulated by caregivers. Technology design based on this groundwork should follow an iterative process that meaningfully engages caregivers and individuals with SIB in naturalistic settings.

Biosensing and Actuation-Platforms Coupling Body Input-Output Modalities for Affective Technologies

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer's felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of "orchestration". By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users' body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~ 75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~ 75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.

Implementation of a school-based Fitbit program for youth with Autism Spectrum Disorder: A feasibility study

BACKGROUND

School settings may be optimal for physical activity interventions for youth with Autism Spectrum Disorder (ASD). Additionally, consumer-based fitness trackers may encourage youth with ASD to increase their physical activity levels, however, no studies have examined whether a fitness tracker program would be feasible in youth with ASD.

OBJECTIVE

To examine the feasibility of a 12-week school-based Fitbit© program for youth with ASD.

METHODS

Six classroom teachers and their students (n = 45) were provided with Fitbit fitness trackers to wear over 12-weeks. Classroom teachers monitored student tracker use and completed open-ended surveys to describe both their experience and their students' experience with the fitness trackers.

RESULTS

Out of the 45 eligible students, 42 (94%) opted to participate in the study. All six teachers and 32 (76%) of the 42 students wore the fitness tracker daily over 12 weeks. Teachers reported that students were most interested in tracking their daily steps, and the short batter life, and account set-up were the biggest challenges to students. All six teachers felt that this program could have long-term sustainability, especially if tracker use could be incorporated into school curriculum and classroom activities.

CONCLUSIONS

A school-based Fitbit program appears to be both feasible, and well-accepted by students with ASD. Future work should evaluate the preliminary efficacy of this type of program.

Bringing the Laboratory Home: PANDABox Telehealth-Based Assessment of Neurodevelopmental Risk in Children

Background

Advances in clinical trials have revealed a pressing need for outcome measures appropriate for children with neurogenetic syndromes (NGS). However, the field lacks a standardized, flexible protocol for collecting laboratory-grade experimental data remotely. To address this challenge, we developed PANDABox (Parent-Administered Neurodevelopmental Assessment), a caregiver-facilitated, remotely administered assessment protocol for collecting integrated and high quality clinical, behavioral, and spectral data relevant to a wide array of research questions. Here, we describe PANDABox development and report preliminary data regarding: (1) logistics and cost, (2) caregiver fidelity and satisfaction, and (3) data quality.

Methods

We administered PANDABox to a cohort of 16 geographically diverse caregivers and their infants with Down syndrome. Tasks assessed attention, language, motor, and atypical behaviors. Behavioral and physiological data were synchronized and coded offline by trained research assistants.

Results

PANDABox required low resources to administer and was well received by families, with high caregiver fidelity (94%) and infant engagement (91%), as well as high caregiver-reported satisfaction (97% positive). Missing data rates were low for video frames (3%) and vocalization recordings (6%) but were higher for heart rate (25% fully missing and 13% partially missing) and discrete behavioral presses (8% technical issues and 19% not enough codable behavior), reflecting the increased technical demands for these activities.

Conclusion

With further development, low-cost laboratory-grade research protocols may be remotely administered by caregivers in the family home, opening a new frontier for cost-efficient, scalable assessment studies for children with NGS other neurodevelopmental disorders.

Robot applications for autism: a comprehensive review

PURPOSE

Technological advances in robotics have brought about exciting developments in different areas such as education, training, and therapy. Recent research has suggested that the robot can be even more effective in rehabilitation, therapy, and education for individuals with Autism Spectrum Disorder (ASD). In this paper, a comprehensive review of robotic technology for children with ASD is presented wherein a large number of journals and conference proceedings in science and engineering search engines' databases were implicated.

MATERIALS AND METHODS

A search for related literature was conducted in three search engines' databases, Web of Science, Scopus, and IEEE Xplore. Thematic keywords were used to identify articles in the recent ten years in titles, keywords, and abstracts. The retrieved articles were filtered, analysed, and evaluated based on specific inclusion and exclusion criteria.

RESULTS

A total of 208 studies were retrieved, while 166 met the inclusion criteria. The selected studies were reviewed according to the type of robot, the participants, objectives, and methods. 68 robots were used in all studies, NAO robot was used in 30.5% of those studies. The total number of participants in all studies was 1671. The highest percentage of the studies reviewed were dedicated to augmenting the learning skills.

CONCLUSIONS

Robots and the associated schemes were used to determine their feasibility and validity for augmenting the learning skills of autistic children. Most of the studies reviewed were focused on improving the social communication skills of autistic children and measuring the extent of robot mitigation of stereotyped autistic behaviours.Implications for rehabilitationSocial robots are not considered as promising tools to be utilized for rehabilitation of autistic children only, but also has been used for children and young people with severe intellectual disability.Rehabilitation for individuals with ASD using robots can augment their cognitive and social skills, but further studies should be conducted to clarify its effectiveness based on other factors such as sex, age and IQ of the participates.Robotic-based rehabilitation is not limited to the physical robots only, but virtual robots have been used also, whereas each of which can be used individually or simultaneously. However, further study is required to assess the extent of its efficiency and effectiveness for both cases.

Dog Phobia Intervention: A Case Study in Improvement of Physiological and Behavioral Symptoms in A Child with Intellectual Disability

Background: Children with intellectual disability are at risk for anxiety disorders involving intense physiological reactions and risky behavioral responses. Interventions have been identified in this field; however, assessment of underlying anxiety is limited and flawed.Method: We implemented a single-subject case study using differential reinforcement to treat dog phobia in a boy with intellectual disability. We recorded elopement and compliance with goals and measured physiological expressions of stress: galvanic skin response, heart rate variability, temperature, and latency to calm down.Results: After fifteen therapy sessions, the boy decreased elopement and noncompliance considerably and showed dramatic improvements in emotional self-regulation.Conclusions: Future research should examine the utility of including biosensing measures in clinical applications and the relationship between physiological measures of anxiety and traditional questionnaires. Children with intellectual disability at risk for anxiety disorders should be tracked longitudinally to examine the effect of interventions on social-emotional well-being and self-regulation.

A Review of Wearable Solutions for Physiological and Emotional Monitoring for Use by People with Autism Spectrum Disorder and Their Caregivers

The goal of real-time feedback on physiological changes, stress monitoring and even emotion detection is becoming a technological reality. People in their daily life experience varying emotional states, some of which are negative and which can lead to decreased attention, decreased productivity and ultimately, reduced quality of life. Therefore, having a solution that continuously monitors the physiological signals of the person and assesses his or her emotional well-being could be a very valuable tool. This paper aims to review existing physiological and motional monitoring devices, highlight their features and compare their sensing capabilities. Such technology would be particularly useful for certain populations who experience rapidly changing emotional states such as people with autism spectrum disorder and people with intellectual disabilities. Wearable sensing devices present a potential solution that can support and complement existing behavioral interventions. This paper presents a review of existing and emerging products in the market. It reviews the literature on state-of-the-art prototypes and analyzes their usefulness, clinical validity, and discusses clinical perspectives. A small number of products offer reliable physiological internal state monitoring and may be suitable for people with Autism Spectrum Disorder (ASD). It is likely that more promising solutions will be available in the near future. Therefore, caregivers should be careful in their selection of devices that meet the care-receiver's personal needs and have strong research support for reliability and validity.

From bedside to bench and back: Translating ASD models

Autism spectrum disorders (ASD) represent a heterogeneous group of disorders defined by deficits in social interaction/communication and restricted interests, behaviors, or activities. Models of ASD, developed based on clinical data and observations, are used in basic science, the "bench," to better understand the pathophysiology of ASD and provide therapeutic options for patients in the clinic, the "bedside." Translational medicine creates a bridge between the bench and bedside that allows for clinical and basic science discoveries to challenge one another to improve the opportunities to bring novel therapies to patients. From the clinical side, biomarker work is expanding our understanding of possible mechanisms of ASD through measures of behavior, genetics, imaging modalities, and serum markers. These biomarkers could help to subclassify patients with ASD in order to better target treatments to a more homogeneous groups of patients most likely to respond to a candidate therapy. In turn, basic science has been responding to developments in clinical evaluation by improving bench models to mechanistically and phenotypically recapitulate the ASD phenotypes observed in clinic. While genetic models are identifying novel therapeutics targets at the bench, the clinical efforts are making progress by defining better outcome measures that are most representative of meaningful patient responses. In this review, we discuss some of these challenges in translational research in ASD and strategies for the bench and bedside to bridge the gap to achieve better benefits to patients.

Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor⁻A Preliminary Study

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.