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Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
[article]
2016
arXiv
pre-print
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Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. ...
Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever ...
Stereotypical Motor Movements (SMMs) in autism (such as body rocking, mouthing, and complex hand movements) [10] can significantly restrict the learning and social interactions. ...
arXiv:1511.01865v3
fatcat:64dumjm42vdntdg6ya5wqiseeq
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging
[article]
2021
arXiv
pre-print
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We create a computer vision classifier for detecting head banging in home videos using a time-distributed convolutional neural network (CNN) in which a single CNN extracts features from each frame in the ...
the train set was in the test set for all folds. ...
ACKNOWLEDGMENTS This work was supported in part by funds to DPW from the National Institutes of Health (1R01EB025025-01, 1R21HD091500-01, 1R01LM013083), the National Science Foundation ...
arXiv:2101.03478v1
fatcat:vqevbhu4ojenbgmfzjbbyro7ni
Computer Vision Techniques for Autism Symptoms Detection and Recognition: A Survey
2020
International Journal of Intelligent Computing and Information Sciences
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Computational technologies, especially computer-vision, machine learning and neural networks techniques have added great advances in detecting autism and these techniques are comprehensively reviewed in ...
Also, medical assisting computer vision-based framework is proposed to detect observable autism symptoms. ...
Stereotypical Motor Movements (SMM) in autism (for example, body shaking, mouthing, and hand flapping [23] ) can delays educational and social development. ...
doi:10.21608/ijicis.2020.46360.1034
fatcat:hn27qhwgsbeahbrnybemzmlwji
Towards Motor-Based Early Detection of Autism Red Flags: Enabling Technology and Exploratory Study Protocol
2021
Sensors
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For this purpose, specialists seek the so-called "red-flags" of motor signature of ASD for more precise diagnostic tests. ...
Observing how children manipulate objects while they are playing can help detect possible autism spectrum disorders (ASD) at an early stage. ...
Neural Network Models Training and Validation As discussed in the previous section, we considered two neural network models, namely TDNN and RNN, to implement the classification of the movements of the ...
doi:10.3390/s21061971
pmid:33799643
fatcat:ahqk7bmkszahjf2blec6kx4ah4
A Novel Deep Learning Approach for Recognizing Stereotypical Motor Movements within and across Subjects on the Autism Spectrum Disorder
2018
Computational Intelligence and Neuroscience
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In this context, we propose a deep learning approach for SMM recognition, namely, convolutional neural networks (CNN) in time and frequency-domains. ...
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties including repetitive patterns of behavior known as stereotypical motor movements (SMM). ...
Research Background on Convolutional Neural Networks. ...
doi:10.1155/2018/7186762
pmid:30111994
pmcid:PMC6077579
fatcat:dvbc3z3p4feuxb4i4cbmo62cce
Machine learning applications to recognize autism and Alzheimer's disease
[chapter]
2019
Neurological Disorders and Imaging Physics, Volume 3
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On the stability of the rectilinear motions in the sextupole rf trap C. M. ...
A simple deposition method for self-assembling single crystalline hybrid perovskite nanostructures Wen-Rong Xie, Bin Liu, Tao Tao et al. ...
The proposed architecture for stereotypical motor movement detection in the static feature space utilized a three-layer CNN. ...
doi:10.1088/978-0-7503-1793-1ch1
fatcat:hoi4lebxzbexzitq3fxkmw7pci
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders
[article]
2017
arXiv
pre-print
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Autism Spectrum Disorders are associated with atypical movements, of which stereotypical motor movements (SMMs) interfere with learning and social interaction. ...
We use a convolutional neural network (CNN) to learn a discriminative feature space from raw data. ...
Acknowledgments The authors would like to thank the members of MPBA lab for their kind collaboration in collecting the simulated data. ...
arXiv:1709.05956v1
fatcat:uz3h6ctyrvhcfm32nhav72ako4
WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
2017
Healthcare
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We have designed an Internet-of-Things (IoT) framework named WearSense that leverages the sensing capabilities of modern smartwatches to detect stereotypic behaviors in children with autism. ...
Autism is a complex developmental disorder that affects approximately 1 in 68 children (according to the recent survey conducted by the Centers for Disease Control and Prevention-CDC) in the U.S., and ...
Mohammadian Rad [5] presented a wireless inertial sensing technology that offers an infrastructure for real-time Stereotypical Motor Movements (SMM) detection. ...
doi:10.3390/healthcare5010011
pmid:28264474
pmcid:PMC5371917
fatcat:qsj55hl3nndevm44ykm2rfokje
Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network
2020
Frontiers in Neuroscience
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Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. ...
Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. ...
The stereotypical motor movements (SMM) in autism patients are body rocking and complex hand movements, which will affect learning and social skills. ...
doi:10.3389/fnins.2019.01325
pmid:32009868
pmcid:PMC6971220
fatcat:r5x54ufskffyvpdwswa6fpvzyy
Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders
2018
Sensors
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Detecting and monitoring of abnormal movement behaviors in patients with Parkinson's Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment ...
abnormal movements in patients with PD and ASD in a novelty detection framework. ...
Spectrum Disorder
FOG
Freezing Of Gait
SMM
Stereotypical Motor Movements
DAE
Denoising Autoencoder
GEVD
Generalized Extreme Value Distribution
CNN
Convolutional Neural Network
ReLU
Rectified ...
doi:10.3390/s18103533
fatcat:il3crauryfct7ogsn7xflypnfy
Using 2D Video-based Pose Estimation for Automated Prediction of Autism Spectrum Disorders in Preschoolers
[article]
2021
medRxiv
pre-print
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For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. ...
Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing ...
Acknowledgments We express our utmost gratitude to all families that took part in this study. We thank our colleagues for their precious contribution to data collection. ...
doi:10.1101/2021.04.01.21254463
fatcat:qj4yhsydrvbphhik56ywncwbty
Hybrid Feature Vector for Screening of Autistic People using Deep Learning
2022
International Journal of Intelligent Engineering and Systems
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FER2013 dataset to train the emotion recognition model using Convolutional Neural Network (CNN). ...
For the purpose of experimentation, we have used Autism Spectrum Quotient AQ10 dataset using fully connected Deep Neural Network (DNN) to train the autism detection model and Facial Expression Recognition ...
Acknowledgments The authors wish to thank the anonymous reviewers for their useful suggestions that helped in improving the quality of this paper. ...
doi:10.22266/ijies2022.0228.35
fatcat:jovtbopxcrbarbrjd2eemqhr2q
Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children
2021
Scientific Reports
Preserved Fulltext
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The file type is application/pdf.
For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. ...
Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing ...
Acknowledgements We express our utmost gratitude to all families that took part in this study. We thank our colleagues for their precious contribution to data collection. ...
doi:10.1038/s41598-021-94378-z
pmid:34301963
pmcid:PMC8302646
fatcat:s7flfjtnrndcjmjn6eeipwbciu
Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019
2020
Translational Psychiatry
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The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research ...
The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. ...
A deep convolutional neural network (DCNN) for feature extraction followed by an SVM for classification has been trained by Shukla et al. 60 to detect whether a person in an image has ASD, cerebral palsy ...
doi:10.1038/s41398-020-01015-w
pmid:32999273
fatcat:7sfld3ry2baznav5c7ztwxhxkq
Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms
2021
Sensors
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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). ...
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. ...
Rad used a convolutional neural network for the detection of SMM using a accelerometer sensor [20] . ...
doi:10.3390/s21103319
pmid:34064750
fatcat:lfzytqzy6vbn7pufuohgogife4
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