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

[EN] 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.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). 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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

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

Skeleton driven action recognition using an image-based spatial-temporal representation and convolution neural network

Individuals with Autism Spectrum Disorder (ASD) typically present difficulties in engaging and interacting with their peers. Thus, researchers have been developing different technological solutions as support tools for children with ASD. Social robots, one example of these technological solutions, are often unaware of their game partners, preventing the automatic adaptation of their behavior to the user. Information that can be used to enrich this interaction and, consequently, adapt the system behavior is the recognition of different actions of the user by using RGB cameras or/and depth sensors. The present work proposes a method to automatically detect in real-time typical and stereotypical actions of children with ASD by using the Intel RealSense and the Nuitrack SDK to detect and extract the user joint coordinates. The pipeline starts by mapping the temporal and spatial joints dynamics onto a color image-based representation. Usually, the position of the joints in the final image is clustered into groups. In order to verify if the sequence of the joints in the final image representation can influence the model’s performance, two main experiments were conducted where in the first, the order of the grouped joints in the sequence was changed, and in the second, the joints were randomly ordered. In each experiment, statistical methods were used in the analysis. Based on the experiments conducted, it was found statistically significant differences concerning the joints sequence in the image, indicating that the order of the joints might impact the model’s performance. The final model, a Convolutional Neural Network (CNN), trained on the different actions (typical and stereotypical), was used to classify the different patterns of behavior, achieving a mean accuracy of 92.4% ± 0.0% on the test data. The entire pipeline ran on average at 31 FPS.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. Vinicius Silva thanks FCT for the PhD scholarship SFRH/BD/SFRH/BD/133314/2017

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

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization