A Multifaceted Device for Discreetly Acquiring Natural Behaviors of Children with Autism

Autism is a multifaceted neurological disorder that affects the four fundamental areas of sensory processing, communication mechanisms, social interaction skills, and whole child/self-esteem. The underlying mechanisms and symptoms of the disorder have been shown to largely vary from patient to patient, and therefore, a durable, effective therapy is best achieved through multifaceted, multidisciplinary approaches that allow a direct assessment of each individual’s behavior, both quantitatively and qualitatively. The aim of this project was to simulate, design, manufacture, and assess a device that can help cultivate sensory, social, communication, and motor skills in autistic children while being able to extract data of the child’s behavior that could be used by the therapist. Critical components of the toy involve auditory and visual stimulation, as well as interactive mechanisms to promote development. The most important features of the toy are hidden cameras that discreetly monitor the child’s reactions in order to provide analytical feedback mechanisms, allowing parents, caregivers, or therapists to monitor and evaluate the child’s learning and therapy. The performance of the toy was examined on 17 children with autism at two specialized centers for child with developmental disorders. The results showed that the device was found satisfactory by the majority of children as assessed by their willingness to spend time accomplishing the tasks on the device, as well as by captured videos of their natural reactions throughout. Furthermore, improved performance was observed on the same population of children who were tested multiple times, indicating the potential use of the toy for therapeutic and learning purposes.Faculty Sponsor: Danial Shahmirzad

A Multifaceted Device for Discreetly Acquiring Natural Behaviors of Children with Autism

Autism is a multifaceted neurological disorder that affects the four fundamental areas of sensory processing, communication mechanisms, social interaction skills, and whole child/self-esteem. The underlying mechanisms and symptoms of the disorder have been shown to largely vary from patient to patient, and therefore, a durable, effective therapy is best achieved through multifaceted, multidisciplinary approaches that allow a direct assessment of each individual’s behavior, both quantitatively and qualitatively. The aim of this project was to simulate, design, manufacture, and assess a device that can help cultivate sensory, social, communication, and motor skills in autistic children while being able to extract data of the child’s behavior that could be used by the therapist. Critical components of the toy involve auditory and visual stimulation, as well as interactive mechanisms to promote development. The most important features of the toy are hidden cameras that discreetly monitor the child’s reactions in order to provide analytical feedback mechanisms, allowing parents, caregivers, or therapists to monitor and evaluate the child’s learning and therapy. The performance of the toy was examined on 17 children with autism at two specialized centers for child with developmental disorders. The results showed that the device was found satisfactory by the majority of children as assessed by their willingness to spend time accomplishing the tasks on the device, as well as by captured videos of their natural reactions throughout. Furthermore, improved performance was observed on the same population of children who were tested multiple times, indicating the potential use of the toy for therapeutic and learning purposes

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype