Biosignal comparison for autism assessment using machine learning models and virtual reality.

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.

Social Signal Processing in Affective Virtual Reality: Human-Shaped Agents Increase Electrodermal Activity in an Elicited Negative Environment.

Prior research on affect elicitation indicates that stimuli with social content (pictures or videos) are more arousing than nonsocial stimuli. In particular, they elicit stronger physiological arousal as measured by electrodermal activity (EDA; i.e., social EDA effect). However, it is unclear how this effect applies to virtual reality (VR), which enables an enhanced sense of presence (SoP) and ecological validity. The study here approached this question from a social-emotional VR framework. A sample of N = 72 participants (55 percent women) experienced a set of six virtual environments (VEs) in the form of emotional parks specifically designed to elicit positive, negative, or neutral affectivity. Half of these VEs included human-shaped agents (social context) and the other half omitted these agents (nonsocial context). The results supported the social EDA effect, which in addition was amplified by the reported SoP. Importantly, the VE featuring a social negative content qualified this observed social EDA effect. The finding is discussed in the light of a negativity bias reported in affect literature, through which negative stimuli typically mobilize attention and bodily activation as a mechanism linked to stress responses. The study's implications extend to the use of VR in both research and practical applications, emphasizing the role of social content in influencing affective and physiological responses.

[Virtual reality and digital biomarkers: a clinical tool for early autism diagnosis]. / Realidad virtual y biomarcadores digitales: una herramienta clínica para el diagnóstico del autismo.

INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which traditional assessment procedures encounter certain limitations. The current ASD research field is exploring and endorsing innovative methods to assess the disorder early on, based on the automatic detection of biomarkers. However, many of these procedures lack ecological validity in their measurements. In this context, virtual reality (VR) shows promise for objectively recording biosignals while users experience ecological situations. METHODS: This study outlines a novel and playful VR procedure for the early assessment of ASD, relying on multimodal biosignal recording. During a VR experience featuring 12 virtual scenes, eye gaze, motor skills, electrodermal activity and behavioural performance were measured in 39 children with ASD and 42 control peers. Machine learning models were developed to identify digital biomarkers and classify autism. RESULTS: Biosignals reported varied performance in detecting ASD, while the combined model resulting from the combination of specific-biosignal models demonstrated the ability to identify ASD with an accuracy of 83% (SD = 3%) and an AUC of 0.91 (SD = 0.04). DISCUSSION: This screening tool may support ASD diagnosis by reinforcing the outcomes of traditional assessment procedures.

Introducción: El Trastorno del Espectro Autista (TEA) es un trastorno del neurodesarrollo, y sus procedimientos tradicionales de evaluación encuentran ciertas limitaciones. El actual campo de investigación sobre TEA está explorando y respaldando métodos innovadores para evaluar el trastorno tempranamente, basándose en la detección automática de biomarcadores. Sin embargo, muchos de estos procedimientos carecen de validez ecológica en sus mediciones. En este contexto, la realidad virtual (RV) presenta un prometedor potencial para registrar objetivamente bioseñales mientras los usuarios experimentan situaciones ecológicas. Métodos: Este estudio describe un novedoso y lúdico procedimiento de RV para la evaluación temprana del TEA, basado en la grabación multimodal de bioseñales. Durante una experiencia de RV con 12 escenas virtuales, se midieron la mirada, las habilidades motoras, la actividad electrodermal y el rendimiento conductual en 39 niños con TEA y 42 compañeros de control. Se desarrollaron modelos de aprendizaje automático para identificar biomarcadores digitales y clasificar el autismo. Resultados: Las bioseñales reportaron un rendimiento variado en la detección del TEA, mientras que el modelo resultante de la combinación de los modelos de las bioseñales demostró la capacidad de identificar el TEA con una precisión del 83% (DE = 3%) y un AUC de 0.91 (DE = 0.04). Discusión: Esta herramienta de detección puede respaldar el diagnóstico del TEA al reforzar los resultados de los procedimientos tradicionales de evaluación.

The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality.

Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children's motor skills. The findings endorsed the hypothesis that when particular goal-directed movements are required, the type of action could modulate the presence of motor abnormalities in ASD. In particular, the ASD motor abnormalities emerged in the task requiring to take with the upper limbs goal-directed actions with low degree of freedom. The motor abnormalities covered (1) the body part mainly involved in the action, and (2) further body parts not directly involved in the movement. Findings were discussed against the background of atypical prospective control of movements and visuomotor discoordination in ASD. These findings contribute to advance the understanding of motor skills in ASD while deepening ecological and objective assessment procedures based on VR.

Virtual humans for ASD intervention: a brief scoping review / Humanos virtuales para la intervención en TEA: una breve revisión del alcance

Abstract Individuals with autism spectrum disorder may present social-communicative and behavioral deficits. Recently, research on treatment and diagnosis has shifted its focus to the application of new tech nologies. Among them is virtual reality, which guarantees a high sense of realism to the experience and allows the implementation of a virtual agent that facilitates the use of the application. In social skills interventions, it has been mostly chosen to implement a virtual agent with a human appearance. Virtual humans guide the user-system interaction through the use of verbal and nonverbal language. They can be equipped with responsiveness: the ability to provide responses to the user based on data recorded during the use of the technology. Responsiveness is functional when the goal is to create an interaction similar to that of everyday life, as it allows for behavioral responses and, at a more sophisticated level, vocal responses. Considering virtual agents capable of holding a conversation with the user, to date three different methods have been implemented that make communication more or less realistic. This brief review proposes a synopsis of relevant virtual humans' features and highlights some key ASD research areas wherein virtual humans are implemented for diagnosis and treatment. A total of 11 studies were selected and their analysis was summarized into 7 main categories. Finally, the clinical and technological implications of the results found were discussed.

Resumen Los individuos con trastorno del espectro autista pueden presentar déficits socio-comunicativos y conductuales. Recientemente, la investigación sobre el tratamiento y el diagnóstico se ha centrado en la aplicación de nuevas tecnologías. Entre ellas se encuentra la realidad virtual, que garantiza un alto sentido de realismo a la experiencia y permite la implementación de un agente virtual que facilite el uso de la aplicación. En las intervenciones de habilidades sociales, se ha optado mayoritariamente por implementar un agente virtual con apariencia humana. Los humanos virtuales guían la interacción usuario-sistema mediante el uso de lenguaje verbal y no verbal. Estos pueden estar dotados de responsividad: la capacidad de proporcionar respuestas al usuario basadas en los datos registrados durante el uso de la tecnología. La responsividad es funcional cuando el objetivo es crear una interacción similar a la de la vida cotidiana, ya que permite dar respuestas conductuales y, a un nivel más sofisticado, respuestas vocales. Considerando los agentes virtuales capaces de mantener una conversación con el usuario, hasta la fecha se han implementado tres métodos diferentes que hacen que la comunicación sea más o menos realista. Esta breve revisión propone una sinopsis de las características de los humanos virtuales relevantes y destaca algunas áreas de investigación clave del TEA en las que se implemen tan humanos virtuales para el diagnóstico y el tratamiento. Se seleccionó un total de 11 estudios y su análisis se resumió en 7 categorías principales. Por último, se discuten las implicaciones clínicas y tecnológicas de los resultados encontrados.

Virtual humans for ASD intervention: a brief scoping review.

Individuals with autism spectrum disorder may present social-communicative and behavioral deficits. Recently, research on treatment and diagnosis has shifted its focus to the application of new technologies. Among them is virtual reality, which guarantees a high sense of realism to the experience and allows the implementation of a virtual agent that facilitates the use of the application. In social skills interventions, it has been mostly chosen to implement a virtual agent with a human appearance. Virtual humans guide the user-system interaction through the use of verbal and nonverbal language. They can be equipped with responsiveness: the ability to provide responses to the user based on data recorded during the use of the technology. Responsiveness is functional when the goal is to create an interaction similar to that of everyday life, as it allows for behavioral responses and, at a more sophisticated level, vocal responses. Considering virtual agents capable of holding a conversation with the user, to date three different methods have been implemented that make communication more or less realistic. This brief review proposes a synopsis of relevant virtual humans' features and highlights some key ASD research areas wherein virtual humans are implemented for diagnosis and treatment. A total of 11 studies were selected and their analysis was summarized into 7 main categories. Finally, the clinical and technological implications of the results found were discussed.

Los individuos con trastorno del espectro autista pueden presentar déficits socio-comunicativos y conductuales. Recientemente, la investigación sobre el tratamiento y el diagnóstico se ha centrado en la aplicación de nuevas tecnologías. Entre ellas se encuentra la realidad virtual, que garantiza un alto sentido de realismo a la experiencia y permite la implementación de un agente virtual que facilite el uso de la aplicación. En las intervenciones de habilidades sociales, se ha optado mayoritariamente por implementar un agente virtual con apariencia humana. Los humanos virtuales guían la interacción usuario-sistema mediante el uso de lenguaje verbal y no verbal. Estos pueden estar dotados de responsividad: la capacidad de proporcionar respuestas al usuario basadas en los datos registrados durante el uso de la tecnología. La responsividad es funcional cuando el objetivo es crear una interacción similar a la de la vida cotidiana, ya que permite dar respuestas conductuales y, a un nivel más sofisticado, respuestas vocales. Considerando los agentes virtuales capaces de mantener una conversación con el usuario, hasta la fecha se han implementado tres métodos diferentes que hacen que la comunicación sea más o menos realista. Esta breve revisión propone una sinopsis de las características de los humanos virtuales relevantes y destaca algunas áreas de investigación clave del TEA en las que se implementan humanos virtuales para el diagnóstico y el tratamiento. Se seleccionó un total de 11 estudios y su análisis se resumió en 7 categorías principales. Por último, se discuten las implicaciones clínicas y tecnológicas de los resultados encontrados.

How priming with body odors affects decision speeds in consumer behavior.

To date, odor research has primarily focused on the behavioral effects of common odors on consumer perception and choices. We report a study that examines, for the first time, the effects of human body odor cues on consumer purchase behaviors. The influence of human chemosignals produced in three conditions, namely happiness, fear, a relaxed condition (rest), and a control condition (no odor), were examined on willingness to pay (WTP) judgments across various products. We focused on the speed with which participants reached such decisions. The central finding revealed that participants exposed to human odors reached decisions significantly faster than the no odor control group. The main driving force is that human body odors activate the presence of others during decision-making. This, in turn, affects response speed. The broader implications of this finding for consumer behavior are discussed.

Intervenciones tecnológicas adaptativas para el trastorno del espectro autista: una revisión bibliográfica / Adaptive technological interventions for autism spectrum disorder: a literature review

Resumen Los individuos con trastornos del espectro autista suelen describirse con deficiencias comunicativas, sociales, emocionales y de comportamiento. A menudo están aislados y son pasivos, con pocas oportunidades de interacción positiva y constructiva con el mundo exterior. Por otra parte, pueden mostrar comportamientos retraídos, estereotipados y disruptivos. Estas condiciones pueden dificultar seriamente sus habilidades adaptativas al ambiente, con consecuencias negativas en su calidad de vida. La heterogeneidad fenotípica y la manifestación, así como la gravedad de los síntomas, pueden variar considerablemente según el niño. Esos determinan la necesidad de intervenciones personalizadas y adaptivas según las necesidades específicas, incluyendo factores como la edad, las habilidades intelectuales y las áreas afectadas específicas. Una intervención temprana promovería las habilidades adaptativas, la autodeterminación y la autonomía hacia el entorno. No obstante, los tiempos de esperas y los costes no permiten una evaluación temprana y como consecuencia los tiempos de intervención se retrasan afectando la cualidad de vida de los niños y de los pa dres. Además, los programas tradicionales de intervención dependen de la experiencia de los terapeutas. Una posible forma de superar este problema es el uso de tecnología adaptativa objetiva según las necesidades. El objetivo de este artículo es proporcionar una visión general de las pruebas empíricas disponible en los últimos siete años. En total, se seleccionaron 8 estudios, con 132 participantes que utilizaron 4 sistemas tecnológicos. Por último, se discuten las implicaciones tecnológicas, clínicas, psicológicas y rehabilitadoras de los hallazgos. Se esbozaron directrices prácticas dentro de esta área temática como perspectivas de investigación futuras.

Abstract Individuals with autistic spectrum disorder are often described as having communication, social, emo tional, and behavioral impairments. They are often isolated and passive, with few opportunities for positive and constructive interaction with the outside world. Moreover, they may exhibit withdrawn, stereotyped and disruptive behaviors. The aforementioned conditions can seriously hamper their ability to adapt to the environment, with negative consequences on their quality of life. Phenotypic heterogeneity and manifestation, as well as symptom severity, can vary greatly from child to child. These determine the need for individualized and adaptive interventions according to specific needs, including factors such as age, intellectual ability, and specific affected areas. Early intervention would promote adaptive skills, self-determination, and autonomy towards the environment. However, wait times and costs do not allow for early assessment, and therefore intervention times are delayed, affecting the quality of life of children and parents. In addition, traditional intervention programs depend on the expertise of the therapists. One possible way to overcome this problem is by using objective adaptive technologies based on needs. This article aims to provide an overview of the empirical evidence available over the past seven years. Overall, 8 studies were selected, with 132 participants using 4 technological systems. Finally, the technological, clinical, psychological, and rehabilitative implications of the findings are discussed. Practical guidelines within this topic area are outlined as future research perspectives.

[Adaptive technological interventions for autism spectrum disorder: a literature review]. / Intervenciones tecnológicas adaptativas para el trastorno del espectro autista: una revisión bibliográfica.

Individuals with autistic spectrum disorder are often described as having communication, social, emotional, nd behavioral impairments. They are often isolated and passive, with few opportunities for positive and constructive interaction with the outside world. Moreover, they may exhibit withdrawn, stereotyped and disruptive behaviors. The aforementioned conditions can seriously hamper their ability to adapt to the environment, with negative consequences on their quality of life. Phenotypic heterogeneity and manifestation, as well as symptom severity, can vary greatly from child to child. These determine the need for individualized and adaptive interventions according to specific needs, including factors such as age, intellectual ability, and specific affected areas. Early intervention would promote adaptive skills, self-determination, and autonomy towards the environment. However, wait times and costs do not allow for early assessment, and therefore intervention times are delayed, affecting the quality of life of children and parents. In addition, traditional intervention programs depend on the expertise of the therapists. One possible way to overcome this problem is by using objective adaptive technologies based on needs. This article aims to provide an overview of the empirical evidence available over the past seven years. Overall, 8 studies were selected, with 132 participants using 4 technological systems. Finally, the technological, clinical, psychological, and rehabilitative implications of the findings are discussed. Practical guidelines within this topic area are outlined as future research perspectives.

Los individuos con trastornos del espectro autista suelen describirse con deficiencias comunicativas, sociales, emocionales y de comportamiento. A menudo están aislados y son pasivos, con pocas oportunidades de interacción positiva y constructiva con el mundo exterior. Por otra parte, pueden mostrar comportamientos retraídos, estereotipados y disruptivos. Estas condiciones pueden dificultar seriamente sus habilidades adaptativas al ambiente, con consecuencias negativas en su calidad de vida. La heterogeneidad fenotípica y la manifestación, así como la gravedad de los síntomas, pueden variar considerablemente según el niño. Esos determinan la necesidad de intervenciones personalizadas y adaptivas según las necesidades específicas, incluyendo factores como la edad, las habilidades intelectuales y las áreas afectadas específicas. Una intervención temprana promovería las habilidades adaptativas, la autodeterminación y la autonomía hacia el entorno. No obstante, los tiempos de esperas y los costes no permiten una evaluación temprana y como consecuencia los tiempos de intervención se retrasan afectando la cualidad de vida de los niños y de los padres. Además, los programas tradicionales de intervención dependen de la experiencia de los terapeutas. Una posible forma de superar este problema es el uso de tecnología adaptativa objetiva según las necesidades. El objetivo de este artículo es proporcionar una visión general de las pruebas empíricas disponible en los últimos siete años. En total, se seleccionaron 8 estudios, con 132 participantes que utilizaron 4 sistemas tecnológicos. Por último, se discuten las implicaciones tecnológicas, clínicas, psicológicas y rehabilitadoras de los hallazgos. Se esbozaron directrices prácticas dentro de esta área temática como perspectivas de investigación futuras.

Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review.

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.

The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.

Machine Learning and Virtual Reality on Body Movements' Behaviors to Classify Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.