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.
Skip Nav Destination
Article navigation
Design Of Medical Devices Conference Abstracts
Detection of Self-Stimulatory Behaviors of Children with Autism Using Wearable and Environmental Sensors
Cheol-Hong. Min,
Cheol-Hong. Min
Department of Electrical and Computer Engineering,
University of Minnesota
, Minneapolis, MN USA
Search for other works by this author on:
Anne Kelly
Anne Kelly
Department of Pediatrics,
University of Minnesota
, Minneapolis, MN USA
Search for other works by this author on:
Cheol-Hong. Min
Department of Electrical and Computer Engineering,
University of Minnesota
, Minneapolis, MN USA
Anne Kelly
Department of Pediatrics,
University of Minnesota
, Minneapolis, MN USAJ. Med. Devices. Jun 2009, 3(2): 027506 (1 pages)
Published Online: June 30, 2009
Article history
Published:
June 30, 2009
Citation
Min, C., Kim, Y., Tewfik, A., and Kelly, A. (June 30, 2009). "Detection of Self-Stimulatory Behaviors of Children with Autism Using Wearable and Environmental Sensors." ASME. J. Med. Devices. June 2009; 3(2): 027506. https://doi.org/10.1115/1.3134931
Download citation file:
Get Email Alerts
Cited By
Related Articles
Separating Aortic Stenosis From Nornal Heart Decision Spaces Using a Quadratic Model
J. Med. Devices (June,2009)
Design of Ultrasonic Motion Analysis System for Estimating Segment's Stabilization During Dynamic Condition
J. Med. Devices (June,2009)
Self-Stimulatory Behavior Detection Using Wearable Sensors for Children With Autistic Spectrum Disorder
J. Med. Devices (June,2008)
Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/Classification
J. Med. Devices (June,2009)
Related Proceedings Papers
Related Chapters
Occlusion Identification and Relief within Branched Structures
Biomedical Applications of Vibration and Acoustics in Therapy, Bioeffect and Modeling
Introduction
Mechanical Blood Trauma in Circulatory-Assist Devices
Role of Artificial Intelligence in Hepatitis B Diagnosis
International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3