Detection of Self-Stimulatory Behaviors of Children with Autism Using Wearable and Environmental Sensors

Abstract

Autism is one of the five pervasive development disorders that may cause severe impairment to a child. Depending on the degree of the symptoms, autism may cause severe impairments in one's social life such as social interaction and communication with other individuals. They may also face challenges in learning, concentrating, sensation and interacting with their surroundings. According to the Center for Disease Control (CDC), 1 in 150 8-year old children in many areas in the United States were diagnosed with autism. It is also known from recent studies that with early diagnosis we can intervene earlier which allows better assistance and treatment. Therefore, it is critical to have an objective assessment tool to assist diagnosis and for management. We have developed an affordable, reliable system that provides evidence based tools for assessment of children with autism. This system can detect various repetitive behavioral patterns often seen in children with autism and enables long term monitoring of repetitive behaviors. Therefore, it can be used to assist doctors, therapists, caregivers and parents with diagnosis and treatment of children with autism. This system incorporates 2 different sensor platforms which include environmental and wearable sensors. The system consists of a 3-axis accelerometer, small microcontroller and a Bluetooth module to transmit data to a base station such as a PC for analysis. We have customized this wearable device to integrate these modules which can be worn by a child. The environmental sensor configuration is composed of a microphone which records the acoustic data of the subject within the room. Using this sensor system, we are able to achieve the necessary information for assessment and therapy in autism research. We have analyzed the 3-axis accelerometer and acoustic data with an intelligent machine learning algorithm. The algorithm extracts time-domain and frequency domain features from the accelerometer data and applies statistical learning techniques to detect repetitive behavioral patterns. For acoustic data, we used sparse signal representation techniques to detect repetitive patterns that indicate vocalization behaviors. We have achieved an average of 89% in classification accuracy for detecting behavioral patterns. Based on the real data collected from children with autism, we were able to detect and recognize four self-stimulatory behaviors of children with autism. In one instance in which a subject had a tantrum, using the correlation between the hand flapping ratio and vocalization intensity, we were able to predict this extreme behavior. Our study opens an application in which devices could be used in a classroom environment to predict extreme behaviors in order that the stress of children with autism could be diverted accordingly so that their actions would be more socially agreeable.