Neuro-cognitive virtual environment for children with autism (VECA).

Autism a neurological disorder which is often diagnosed during early childhood and can cause significant social, communication, and behavioral challenges over a lifetime. It is increasing day by day and people are inclining from clinical and psychological therapies to assistive technologies. We have developed an interactive virtual environment VECA that aims to enhance the cognitive skills and creativity in children with autism by playing games and interacting with the environment. The setup also incorporates the feedback of the child that whether he/she is comfortable with the environment or not. This solution is cost effective, with no side effects unlike traditional therapies, and can provide valuable insight to the behavior analysis of the autism patients

How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder

Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy

Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study

Mobile digital therapeutics for autism spectrum disorder (ASD) often target emotion recognition and evocation, which is a challenge for children with ASD. While such mobile applications often use computer vision machine learning (ML) models to guide the adaptive nature of the digital intervention, a single model is usually deployed and applied to all children. Here, we explore the potential of model personalization, or training a single emotion recognition model per person, to improve the performance of these underlying emotion recognition models used to guide digital health therapies for children with ASD. We conducted experiments on the Emognition dataset, a video dataset of human subjects evoking a series of emotions. For a subset of 10 individuals in the dataset with a sufficient representation of at least two ground truth emotion labels, we trained a personalized version of three classical ML models on a set of 51 features extracted from each video frame. We measured the importance of each facial feature for all personalized models and observed differing ranked lists of top features across subjects, motivating the need for model personalization. We then compared the personalized models against a generalized model trained using data from all 10 participants. The mean F1-scores achieved by the personalized models were 90.48%, 92.66%, and 86.40%, respectively. By contrast, the mean F1-scores reached by non-personalized models trained on different human subjects and evaluated using the same test set were 88.55%, 91.78%, and 80.42%, respectively. The personalized models outperformed the generalized models for 7 out of 10 participants. PCA analyses on the remaining 3 participants revealed relatively facial configuration differences between emotion labels within each subject, suggesting that personalized ML will fail when the variation among data points within a subjects data is too low

Tracking Visible Features of Speech for Computer-Based Speech Therapy for Childhood Apraxia of Speech

At present, there are few, if any, effective computer-based speech therapy systems (CBSTs) that support the at-home component for clinical interventions for Childhood Apraxia of Speech (CAS). PROMPT, an established speech therapy intervention for CAS, has the potential to be supported via a CBST, which could increase engagement and provide valuable feedback to the child. However, the necessary computational techniques have not yet been developed and evaluated. In this thesis, I will describe the development of some of the key underlying computational components that are required for the development of such a system. These components concern camera-based tracking of visible features of speech which concern jaw kinematics. These components would also be necessary for the serious game that we have envisioned

Enhancing the measurement of clinical outcomes using Microsoft Kinect

There is a growing body of applications leveraging Microsoft Kinect and the associated Windows Software Development Kit in health and wellness. In particular, this platform has been valuable in developing interactive solutions for rehabilitation including creating more engaging exercise regimens and ensuring that exercises are performed correctly for optimal outcomes. Clinical trials rely upon robust and validated methodologies to measure health status and to detect treatment-related changes over time to enable the efficacy and safety of new drug treatments to be assessed and measured. In many therapeutic areas, traditional outcome measures rely on subjective investigator and patient ratings. Subjective ratings are not always sensitive to detecting small improvements, are subject to inter- and intra-rater variability and limited in their ability to record detailed or subtle aspects of movement and mobility. For these reasons, objective measurements may provide greater sensitivity to detect treatment-related changes where they exist. In this review paper, we explore the use of the Kinect platform to develop low-cost approaches to objectively measure aspects of movement. We consider published applications that measure aspects of gait and balance, upper extremity movement, chest wall motion and facial analysis. In each case, we explore the utility of the approach for clinical trials, and the precision and accuracy of estimates derived from the Kinect output. We conclude that the use of games platforms such as Microsoft Kinect to measure clinical outcomes offer a versatile, easy to use and low-cost approach that may add significant value and utility to clinical drug development, in particular in replacing conventional subjective measures and providing richer information about movement than previously possible in large scale clinical trials, especially in the measurement of gross spatial movements. Regulatory acceptance of clinical outcomes collected in this way will be subject to comprehensive assessment of validity and clinical relevance, and this will require good quality peer-reviewed publications of scientific evidence

Assisting therapists in assessing small animal phobias by computer analysis of video-recorded sessions

Behavioural Avoidance Tests (BATs) are commonly used for assessing phobias. While easy to deploy, these tests have some practical difficulties. For instance, therapists have to make distance estimations that are hard to do with accuracy and objectivity; or information regarding the performance of the patients (e.g. their walking pattern) is lost. To alleviate these difficulties, a computerized tool has been developed to extract the walking pattern of patients while approaching the phobic stimulus. From a video-recorded BAT session, two visual representations have been explored to compactly summarize the patient’s behavior: a static one (an image) and a dynamic one (an animation). A proof-of-concept prototype has been tested with 23 therapists. Most of the therapists preferred the animated representation, since it provides them with a better sense of the dynamics of how the patient really behaved. The participants agreed that this tool might be useful in assisting therapist when assessing phobia through BATs, since diagnostics could be made in a more accurate and objective way.This work has been partly supported by Fundació Caixa-Castelló (through grant P1-1A2010-11) and Generalitat Valenciana (through grant PROMETEOII2014062)