Mensaje de error

El archivo especificado temporary://fileto431U no se pudo copiar, porque el directorio de destino no está configurado correctamente. La causa puede ser un problema de permisos en el directorio o los archivos. Hay más información disponible en el registro del sistema.

Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles

@inproceedings{senanayake2014artificial,
  title={Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles},
  author={Senanayake, SMN and Triloka, Joko and Malik, Owais A and Iskandar, Mohammad},
  booktitle={Neural Networks (IJCNN), 2014 International Joint Conference on},
  pages={3503--3510},
  year={2014},
  organization={IEEE}
}

 

Abstract:

The objective of this study is to investigate the use of electromyography (EMG) signals and video based soft tissue deformation (STD) analysis for identifying the gait patterns of healthy and injured subjects. The system includes a wireless surface electromyography (EMG) sensor unit and two video camera systems for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network based intelligent system software for identifying the gait patterns of subjects during walking activity. The system uses root mean square (RMS) value of EMG signals and soft tissue deformation parameter (STDP) as the input features. In order to estimate the STD during a muscular contraction while walking, flexible triangular meshes are built on reference points. The positions of these selected points are evaluated by applying the block matching motion estimation technique. Based on the extracted features, multilayer feed-forward backpropagation networks (FFBPNNs) with different network training functions were designed and their classification performances were compared. The system has been tested for a group of healthy and injured subjects. The results showed that FFBPNN with Levenberg-Marquardt training function provided better prediction behavior (98% overall accuracy) as compared to FFBPNN with other training functions for gait patterns identification based on RMS value of EMG and STDP.

ESTE TRABAJO ES MUY PARECIDO A LO QUE PROPONEMOS EN LA LINEA DE MEDICIÓN MUSCULAR POR VISIÓN COMPUTACIONAL SOLO QUE ACA USAN EMG COMO INFO COMPLEMENTARIA PARA CLASIFICAR PATRONES DE CAMINATA Y NOSOTROS PRETENDEMOS CORRELACIONAR, PERO LA STDP COMO FEATURE ES USADA Y PARACE EL ANTECEDENTE A SEGUIR

The work of \cite{senanayake2014artificial} investigate the use of surface Electromiography (EMG) and video based soft tissue deformation analysys to identify gait patterns and classify them as from healthy or injured subjects.
The systems consist in capturing simultaneously surface EMG (wireless) and video (two cameras) of subjects during gait.
The RMS of EMG and the STDP as extracted in \cite{carli2006study,goffredo2005evaluation} are used as input to  multilayer feed fowrward back propagation networks. This networks were trained using different functions in order to evaluate their performance
As a conclusion the authors says that the combination of rms EMG and soft tissue defromation parameter are useful for identifying gait patterns of injured and healthy subjects.