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Andres Ruiz-Olaya   Professor  University Educator/Researcher 
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Andres Ruiz-Olaya published an article in January 2015.
Top co-authors
Teodiano Bastos-Filho

110 shared publications

Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria 29075-910, Brazil

Alexander Cerquera

13 shared publications

Facultad de Ingeniería Electrónica y Biomédica–Research Group Complex Systems, Universidad Antonio Nariño, Bogota, Colombia

I. D. Plazas-Roa

2 shared publications

Universidad Antonio Narino

Jan Boelts

1 shared publications

University of Osnabrueck

4
Publications
16
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Distribution of Articles published per year 

Total number of journals
published in
 
3
 
Publications
BOOK-CHAPTER 5 Reads 0 Citations A Tele-robotic System for Real-Time Remote Evaluation of Upper-Limb Function Iván D. Plazas-Roa, Andrés F. Ruiz-Olaya Published: 01 January 2015
IFMBE Proceedings, doi: 10.1007/978-3-319-13117-7_60
DOI See at publisher website
BOOK-CHAPTER 4 Reads 0 Citations Toward an Upper-Limb Neurorehabilitation Platform Based on FES-Assisted Bilateral Movement: Decoding User’s Intentionali... Andrés Felipe Ruíz-Olaya, Alberto López-Delis, Alexander Cer... Published: 01 January 2015
Algorithms and Discrete Applied Mathematics, doi: 10.1007/978-3-319-18914-7_15
DOI See at publisher website
BOOK-CHAPTER 3 Reads 0 Citations Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application Jan Boelts, Alexander Cerquera, Andrés Felipe Ruíz-Olaya Published: 01 January 2015
Algorithms and Discrete Applied Mathematics, doi: 10.1007/978-3-319-18914-7_16
DOI See at publisher website
BOOK-CHAPTER 4 Reads 1 Citation A Comparison of Myoelectric Pattern Recognition Methods to Control an Upper Limb Active Exoskeleton Alberto López-Delis, Andrés Felipe Ruíz-Olaya, Teodiano Frei... Published: 01 January 2013
Ambient Intelligence, doi: 10.1007/978-3-642-41827-3_13
DOI See at publisher website ABS Show/hide abstract
Physically impaired people may use Surface Electromyography (sEMG) signals to control assistive devices in an automatic way. sEMG signals directly reflect the human motion intention, they can be used as input information for active exoskeleton control. This paper proposes a set of myoelectric algorithms based on machine learning for detecting movement intention aimed at controlling an upper limb active exoskeleton. The algorithms use a feature extraction stage based on a combination of time and frequency domain features (mean absolute value – waveform length, and auto-regressive model, respectively). The pattern recognition stage uses Linear Discriminant Analysis, K-Nearest Neighbor, Support Vector Machine and Bayesian classifiers. Additionally, two post-processing techniques are incorporated: majority vote and transition removal. The performance of the algorithms is evaluated with parameters of sensitivity, specificity, positive predictive value, error rate and active error rate, under typical conditions. These evaluations allow identifying pattern recognition algorithms for real-time control of an active exoskeleton.
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