This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol. Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms and an accurate diagnosis. In this work, we contribute a new database consisting of video clips of typical (normal) and atypical (such as hand flapping, spinning or rocking) behaviors, displayed in natural settings, which have been collected from the YouTube video website. We propose a preliminary non-intrusive approach based on skeleton keypoint identification using pretrained deep neural networks on human body video clips to extract features and perform body movement analysis that differentiates typical and atypical behaviors of children. Experimental results on the newly contributed database show that our platform performs best with decision tree as the classifier when compared to other popular methodologies and offers a baseline against which alternate approaches may developed and tested.
In recent years there has been significant investigation into the use of computer vision methods to identify and classify a number of behavioral atypicalities, including non-intrusive (at a distance) video based monitoring of epileptic seizures in children @cite_10 @cite_17 @cite_6 , human activity recognition @cite_28 @cite_5 and assisting social interactions at various levels @cite_9 @cite_15 @cite_25 @cite_4 @cite_11 @cite_14 . The advantage of computer vision techniques over certain other methods which may involve placement of measuring devices on the subjects body is the non-invasiveness (without interfering the patients) of the method @cite_26 , a carefully placed camera is less likely to interfere with the session and likewise will reduce stress on the child in question. Such non-intrusiveness (measurement at a distance) would not alter the usual natural behavior of the child, thereby, improves the effectiveness of ASD assessment. These attributes are highly desirable by the clinicians and practitioners in the evaluation of ASD among children.
@cite_11 @cite_4 @cite_14 @cite_26 @cite_28 @cite_9 @cite_6 @cite_5 @cite_15 @cite_10 @cite_25 @cite_17
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In this work we propose a non-intrusive video analytic system for patient's body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients' heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally non-intrusive. The user initializes the person-specific skin color and selects few face head poses in the initial few frames. The system then tracks the head face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection. Clinical studies confirm that mental illnesses such as autism, Obsessive Compulsive Disorder (OCD), etc. show behavioral abnormalities even at very young ages; the early diagnosis of which can help steer effective treatments. Most often, the behavior of such at-risk children deviate in very subtle ways from that of a normal child; correct diagnosis of which requires prolonged and continuous monitoring of their activities by a clinician, which is a difficult and time intensive task. As a result, the development of automation tools for assisting in such monitoring activities will be an important step towards effective utilization of the diagnostic resources. In this paper, we approach the problem from a computer vision standpoint, and propose a novel system for the automatic monitoring of the behavior of children in their natural environment through the deployment of multiple non-invasive sensors (cameras and depth sensors). We provide details of our system, together with algorithms for the robust tracking of the activities of the children. Our experiments, conducted in the Shirley G. Moore Laboratory School, demonstrate the effectiveness of our methodology. Computer vision technology has a unique opportunity to impact that study of children’s behavior, by providing a means to automatically capture behavioral data in an noninvasive manner and analyze behavioral interactions between children and their caregivers and peers. We briefly outline a research agenda in Behavior Imaging, which targets the capture and analysis of social and communicative behaviors. We present illustrative results from an on-going project on the content-based retrieval of social games between children and adults from an unstructured video corpus. In this paper, we propose a “Response to Name Dataset” for autism spectrum disorder (ASD) study as well as a multimodal ASD auxiliary screening system based on machine learning. ASD children are characterized by their impaired interpersonal communication abilities and lack of response. In the proposed dataset, the reactions of children are recorded by cameras upon calling their names. The responsiveness of each child is then evaluated by a clinician with a score among 0, 1 and 2 following the Autism Diagnostic Observation Schedule (ADOS). We then develop a rule-based multimodal framework to quantitatively evaluate each child. Our system involves speech recognition based automatic name calling detection, face detection alignment, head pose estimation, and considers the response speed, eye contact duration and head orientation to output the final prediction. Compared to existing work, our dataset characterizes a more precise and detailed scoring system with clinical trial standards, as well as a more spontaneous setting by incorporating less lab-controlled sessions with dynamic cluttered environments, multi-pose mobile captured videos, and flexible number of accompanying adults. Experiments show that our machine predicted scores align closely with human professional diagnosis, showing promising potential in early screening of ASD, and shedding light on future clinical applications.