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Using a Support-Vector Machine Algorithm to Classify Lower Extremity EMG Signals During Running Shod/Unshod With Different Foot Strike Patterns.
Pires, Ricardo; Falcari, Thays; Campo, Alexandre B; Pulcineli, Bárbara C; Hamill, Joseph; Ervilha, Ulysses F.
Afiliação
  • Pires R; 1 Laboratório de Controle Aplicado, Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, São Paulo, SP, BR.
  • Falcari T; 1 Laboratório de Controle Aplicado, Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, São Paulo, SP, BR.
  • Campo AB; 1 Laboratório de Controle Aplicado, Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, São Paulo, SP, BR.
  • Pulcineli BC; 2 Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, SP, BR.
  • Hamill J; 3 Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.
  • Ervilha UF; 2 Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, SP, BR.
J Appl Biomech ; 35(1): 87­90, 2019 02 01.
Article em En | MEDLINE | ID: mdl-30207195
The present study aimed to use a Support Vector Machine (SVM) algorithm to identify and classify shod and barefoot running as well as rearfoot and forefoot landings. Ten habitually shod runners ran at self-selected speed. Thigh and leg muscle surface electromyography (EMG) were recorded. Discrete Wavelet transformation (DWT) and Fast Fourier transformation (FFT) were used for the assembly of vectors for training and classification of a SVM. Using the FFT coefficients for the gastrocnemius and tibialis anterior muscles presented the best results for differentiating between rearfoot/forefoot running in the window before foot-floor contact possibly due to these muscles' critical role in determining which part of the foot will first touch the floor. The classification rate was 76% and 67% respectively, with a probability of being random of 0.5% and 4% respectively. For the same terms and conditions of classification, the DWT produced a reduction in the percentage of correctness of 60% and 53% with a probability of having reached these levels randomly of 15% and 35%. In conclusion, based on EMG signals, the use a FFT to train a SVM was a better option to differentiate running forefoot/rearfoot than to use the DWT. Shod/barefoot running could not be differentiated.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Appl Biomech Ano de publicação: 2019 Tipo de documento: Article País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Appl Biomech Ano de publicação: 2019 Tipo de documento: Article País de publicação: Estados Unidos