Reach and throw movement analysis with support vector machines in early diagnosis of autism

Autor: Sara Forti, Paolo Perego, Alessandro Crippa, Angela Valli, Gianluigi Reni
Přispěvatelé: Perego, P, Forti, S, Crippa, A, Valli, A, Reni, G
Rok vydání: 2009
Předmět:
Engineering
Motion analysis
Movement
Biomedical Engineering
Reproducibility of Result
Machine learning
computer.software_genre
Pattern Recognition
Automated
Gait (human)
Artificial Intelligence
Early Diagnosi
medicine
Humans
Autistic Disorder
Gait
Artificial neural network
Hand Strength
business.industry
Movement (music)
Medicine (all)
Reproducibility of Results
Pattern recognition
Signal Processing
Computer-Assisted
Cell Biology
Equipment Design
Neural Networks (Computer)
medicine.disease
Biomechanical Phenomena
Algorithm
Support vector machine
Early Diagnosis
Case-Control Studies
Child
Preschool
Pattern recognition (psychology)
Radial basis function kernel
Autism
Artificial intelligence
Neural Networks
Computer
Case-Control Studie
business
computer
Algorithms
Software
Human
Developmental Biology
Zdroj: Scopus-Elsevier
ISSN: 2375-7477
Popis: Movement disturbances play an intrinsic part in autism. Upper limb movements like reach-and-throw seem to be helpful in early identification of children affected by autism. Nevertheless few works investigate the application of classifying methods to upper limb movements. In this study we used a machine learning approach Support Vector Machine (SVM) for identifying peculiar features in reach-and-throw movements. 10 pre-scholar age children with autism and 10 control subjects performing the same exercises were analyzed. The SVM algorithm proved to be able to separate the two groups: accuracy of 100% was achieved with a soft margin algorithm, and accuracy of 92.5% with a more conservative one. These results were obtained with a radial basis function kernel, suggesting that a non-linear analysis is possibly required. ©2009 IEEE.
Databáze: OpenAIRE
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