Abstract
Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child’s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors’ data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.
Highlights
Autism spectrum disorder, commonly called autism, is defined as a variety of disorders, which include challenges with social rules, difficulty in verbal and non-verbal communication, and restricted or repetitive actions [1]
Autistic people face many challenges in their daily lives in areas such as social skills, repetitive behaviors, speech, and nonverbal communication, and experience feelings of hesitation. They use fewer communicative gestures compared with typically developing children (TD), so they struggle to convey their ideas or thoughts with words, gestures, or facial expressions
Some studies focused on the autism spectrum disorder (ASD) subject, their types of gesture used, and how they behave while communicating with others
Summary
Commonly called autism, is defined as a variety of disorders, which include challenges with social rules, difficulty in verbal and non-verbal communication, and restricted or repetitive actions [1]. Autistic people face many challenges in their daily lives in areas such as social skills, repetitive behaviors, speech, and nonverbal communication, and experience feelings of hesitation They use fewer communicative gestures compared with typically developing children (TD), so they struggle to convey their ideas or thoughts with words, gestures, or facial expressions. Due to nonverbal communication or repetitive speaking, ASD children have difficulties conveying their message and other people struggle to understand their gestures. In order to cope with these challenges, the novel contributions in this paper are as follows: Since ASD is a special body condition, both medically and physically, we did not use the data of normal people to train the supervised machine learning algorithm for the gestures recognition of ASD.
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