Applying machine learning to identify autistic adults using imitation: An exploratory study

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism

Using movement kinematics to understand the motor side of Autism Spectrum Disorder

openComprensione del sintomo motorio dell'autismo attraverso la cinematica del movimentoBeside core deficits in social interaction and communication, atypical motor patterns have been often reported in people with Autism Spectrum Disorder (ASD). It has been recently speculated that a part of these sensorimotor abnormalities could be better explained considering prospective motor control (i.e., the ability to plan actions toward future events or consider future task demands), which has been hypothesized to be crucial for higher mind functions (e.g., understand intentions of other people) (Trevarthen and Delafield-Butt 2013). The aim of the current dissertation was to tackle the motor ‘side’ in ASD exploring whether and how prospective motor control might be atypical in children with a diagnosis of autism, given that actions are directed into the future and their control is based on knowledge of what is going to happen next (von Hofsten and Rosander 2012). To do this, an integrative approach based on neuropsychological assessment, behavioural paradigms and machine learning modelling of the kinematics recorded with motion capture techniques was applied in typically developing children and children with ASD without accompanying intellectual impairment.openXXXI CICLO - ARCHITETTURA E DESIGN - Design navale e nauticoBECCHIO, CRISTINA (IIT)Podda, Jessic

Enactivism, other minds, and mental disorders

Although enactive approaches to cognition vary in terms of their character and scope, all endorse several core claims. The first is that cognition is tied to action. The second is that cognition is composed of more than just in-the-head processes; cognitive activities are externalized via features of our embodiment and in our ecological dealings with the people and things around us. I appeal to these two enactive claims to consider a view called “direct social perception” : the idea that we can sometimes perceive features of other minds directly in the character of their embodiment and environmental interactions. I argue that if DSP is true, we can probably also perceive certain features of mental disorders as well. I draw upon the developmental psychologist Daniel Stern’s notion of “forms of vitality”—largely overlooked in these debates—to develop this idea, and I use autism as a case study. I argue further that an enactive approach to DSP can clarify some ways we play a regulative role in shaping the temporal and phenomenal character of the disorder in question, and it may therefore have practical significance for both the clinical and therapeutic encounter

Motion and emotion estimation for robotic autism intervention.

Robots have recently emerged as a novel approach to treating autism spectrum disorder (ASD). A robot can be programmed to interact with children with ASD in order to reinforce positive social skills in a non-threatening environment. In prior work, robots were employed in interaction sessions with ASD children, but their sensory and learning abilities were limited, while a human therapist was heavily involved in “puppeteering” the robot. The objective of this work is to create the next-generation autism robot that includes several new interactive and decision-making capabilities that are not found in prior technology. Two of the main features that this robot would need to have is the ability to quantitatively estimate the patient’s motion performance and to correctly classify their emotions. This would allow for the potential diagnosis of autism and the ability to help autistic patients practice their skills. Therefore, in this thesis, we engineered components for a human-robot interaction system and confirmed them in experiments with the robots Baxter and Zeno, the sensors Empatica E4 and Kinect, and, finally, the open-source pose estimation software OpenPose. The Empatica E4 wristband is a wearable device that collects physiological measurements in real time from a test subject. Measurements were collected from ASD patients during human-robot interaction activities. Using this data and labels of attentiveness from a trained coder, a classifier was developed that provides a prediction of the patient’s level of engagement. The classifier outputs this prediction to a robot or supervising adult, allowing for decisions during intervention activities to keep the attention of the patient with autism. The CMU Perceptual Computing Lab’s OpenPose software package enables body, face, and hand tracking using an RGB camera (e.g., web camera) or an RGB-D camera (e.g., Microsoft Kinect). Integrating OpenPose with a robot allows the robot to collect information on user motion intent and perform motion imitation. In this work, we developed such a teleoperation interface with the Baxter robot. Finally, a novel algorithm, called Segment-based Online Dynamic Time Warping (SoDTW), and metric are proposed to help in the diagnosis of ASD. Social Robot Zeno, a childlike robot developed by Hanson Robotics, was used to test this algorithm and metric. Using the proposed algorithm, it is possible to classify a subject’s motion into different speeds or to use the resulting SoDTW score to evaluate the subject’s abilities

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

The cerebellum and motor dysfunction in neuropsychiatric disorders

The cerebellum is densely interconnected with sensory-motor areas of the cerebral cortex, and in man, the great expansion of the association areas of cerebral cortex is also paralleled by an expansion of the lateral cerebellar hemispheres. It is therefore likely that these circuits contribute to non-motor cognitive functions, but this is still a controversial issue. One approach is to examine evidence from neuropsychiatric disorders of cerebellar involvement. In this review, we narrow this search to test whether there is evidence of motor dysfunction associated with neuropsychiatric disorders consistent with disruption of cerebellar motor function. While we do find such evidence, especially in autism, schizophrenia and dyslexia, we caution that the restricted set of motor symptoms does not suggest global cerebellar dysfunction. Moreover, these symptoms may also reflect involvement of other, extra-cerebellar circuits and detailed examination of specific sub groups of individuals within each disorder may help to relate such motor symptoms to cerebellar morphology

Sensorimotor Differences in Autism Spectrum Disorder: An evaluation of potential mechanisms.

This thesis examined the aetiology of sensorimotor impairments in Autism Spectrum Disorder: a neurodevelopmental condition that affects an individual’s socio-behavioural preferences, personal independence, and quality of life. Issues relating to clumsiness and movement coordination are common features of autism that contribute to wide-ranging daily living difficulties. However, these characteristics are relatively understudied and there is an absence of evidence-based practical interventions. To pave the way for new, scientifically-focused programmes, a series of studies investigated the mechanistic underpinnings of sensorimotor differences in autism. Following a targeted review of previous research, study one explored links between autistic-like traits and numerous conceptually-significant movement control functions. Eye-tracking analyses were integrated with force transducers and motion capture technology to examine how participants interacted with uncertain lifting objects. Upon identifying a link between autistic-like traits and context-sensitive predictive action control, study two replicated these procedures with a sample of clinically-diagnosed participants. Results illustrated that autistic people are able to use predictions to guide object interactions, but that uncertainty-related adjustments in sensorimotor integration are atypical. Such findings were advanced within a novel virtual-reality paradigm in study three, which systematically manipulated environmental uncertainty during naturalistic interception actions. Here, data supported proposals that precision weighting functions are aberrant in autistic people, and suggested that these individuals have difficulties with processing volatile sensory information. These difficulties were not alleviated by the experimental provision of explicit contextual cues in study four. Together, these studies implicate the role of implicit neuromodulatory mechanisms that regulate dynamic sensorimotor behaviours. Results support the development of evidence-based programmes that ‘make the world more predictable’ for autistic people, with various theoretical and practical implications presented. Possible applications of these findings are discussed in relation to recent multi-disciplinary research and conceptual advances in the field, which could help improve daily living skills and functional quality of life.Economic and Social Research Council (ESRC

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings