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.

Perego, P., Forti, S., Crippa, A., Valli, A., Reni, G. (2009). Reach and throw movement analysis with Support Vector Machines in early diagnosis of autism. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp.2555-2558) [10.1109/IEMBS.2009.5335096].

Reach and throw movement analysis with Support Vector Machines in early diagnosis of autism

CRIPPA, ALESSANDRO;
2009

Abstract

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.
poster + paper
Algorithms; Artificial Intelligence; Autistic Disorder; Biomechanical Phenomena; Case-Control Studies; Child, Preschool; Early Diagnosis; Equipment Design; Gait; Hand Strength; Humans; Movement; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Software; Signal Processing, Computer-Assisted; Cell Biology; Developmental Biology; Biomedical Engineering; Medicine (all)
English
31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
2009
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
9781424432967
2009
2009
2555
2558
5335096
none
Perego, P., Forti, S., Crippa, A., Valli, A., Reni, G. (2009). Reach and throw movement analysis with Support Vector Machines in early diagnosis of autism. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp.2555-2558) [10.1109/IEMBS.2009.5335096].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/130820
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