Corpus ID: 16937015

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

@article{Rad2015ConvolutionalNN,
  title={Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism},
  author={Nastaran Mohammadian Rad and Andrea Bizzego and Seyed Mostafa Kia and Giuseppe Jurman and Paola Venuti and Cesare Furlanello},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.01865}
}
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and… Expand
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References

SHOWING 1-10 OF 24 REFERENCES
Detecting stereotypical motor movements in the classroom using accelerometry and pattern recognition algorithms
TLDR
Activity recognition results for stereotypical hand flapping and body rocking are presented using accelerometer data collected wirelessly from six children with ASD repeatedly observed by experts in real classroom settings and preliminary results indicate that non-expert annotations for training can be as effective as expert annotations. Expand
Moving towards a real-time system for automatically recognizing stereotypical motor movements in individuals on the autism spectrum using wireless accelerometry
TLDR
It is concluded that real-time, person-dependent, adaptive algorithms are needed in order to accurately and consistently measure SMM automatically in individuals on the autism spectrum over time in real-word settings. Expand
Stereotype movement recognition in children with ASD
Abstract Autism Spectrum Disorders (ASD) manifest in different behaviors, being one of them body rocking, mouthing, and complex hand and finger movements [1] . The traditional methods for recordingExpand
Novel pattern detection in children with Autism Spectrum Disorder using Iterative Subspace Identification
  • Cheol-Hong Min, A. Tewfik
  • Computer Science
  • 2010 IEEE International Conference on Acoustics, Speech and Signal Processing
  • 2010
TLDR
Novel methods to assist management by automatically detecting stereotypical behavioral patterns using accelerometer data using the Iterative Subspace Identification (ISI) algorithm to learn subspaces in which the sensor data lives. Expand
Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum
TLDR
Activity recognition results for stereotypical hand flapping and body rocking are presented using data collected from six children with ASD repeatedly observed in both laboratory and classroom settings. Expand
Optimal sensor location for body sensor network to detect self-stimulatory behaviors of children with autism spectrum disorder
TLDR
A wearable sensor system that uses a 3 axis accelerometer to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder is developed and it is shown that using single sensor on the back achieves 95.5% classification rate for rocking and 80. Expand
Automatic detection of stereotyped hand flapping movements: Two different approaches
TLDR
This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic and facilitates the identification of relevant behavioral patterns when studying interaction skills in children with ASD. Expand
Automated Detection of Stereotypical Motor Movements
TLDR
The use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD revealed that, on average,pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Expand
Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data
  • Cheol-Hong Min, A. Tewfik
  • Computer Science, Medicine
  • 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
  • 2010
TLDR
This paper shows time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events and shows novel event detection using online dictionary update method. Expand
Automatic assessment of problem behavior in individuals with developmental disabilities
TLDR
It is demonstrated how machine learning techniques can be used to segment relevant behavioral episodes from a continuous sensor stream and to classify them into distinct categories of severe behavior (aggression, disruption, and self-injury). Expand
...
1
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3
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