Browsing by Author "Astudillo Palomeque, Felipe Emmanuel"

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Detección de la intención de movimiento de extremidades inferiores usando métodos de aprendizaje supervisado
(2019-04-08) Astudillo Palomeque, Felipe Emmanuel; Charry Villamagua, José Fernando; De Balzan, Sara Wong; Minchala Ávila, Luis Ismael
This work is part of the project Prototype of usable exoskeleton in the lower extremities, through the use of adaptive control algorithms. The aim of this project was to develop a capable algorithm of detecting the motion intention based on electromyograms (EMG) of subjects with pathologies in the lower limbs using artificial neural networks (ANN) with pattern recognition and the Levenberg-Marquardt method. Contemplated a stage (filtering, rectification and normalization) and the annotation of the motion intention for EMG pre-processing. Trained and validated the algorithm using an EMG database of normal subjects. Obtained an overall performance of 90.96% for a point-to-point evaluation and 94.88% in an evaluation by events. Publishing these results for ETCM-IEEE2018. Recorded a database of six patients (42.83 ± 10.51 years), containing 78 EMG signals, corresponding to 13 muscles. With the training parameters obtained in the first database, determined the motion intention in the subjects with pathologies, additionally, the values of signal-to-noise ratio (SNR) and mean frequency (MNF). Obtained an overall performance of 93.14% point-to-point and 91.19% by events, the delay time was 31.06 ± 18.89 ms, SNR of 17.28 ± 1.67 dB and the MNF values found they are lower than those the literature reported, suggesting lower torque in this population. These results allow contemplating the implementation of the algorithm in real time for an exoskeleton.
Lower limbs motion intention detection by using pattern recognition
(Institute of Electrical and Electronics Engineers Inc., 2018) Astudillo Palomeque, Felipe Emmanuel; Charry Ramírez, José Ricardo; Minchala Ávila, Luis Ismael; Wong de balzan , Sara Null
Electromyographic (EMG) signals processing allows to perform the detection of the intention of movement of the limbs of the human body in order to further use this decision to control wearable devices. For instance, robotic exoskeletons main objective consist of a human-robot interface capable of understanding the user’s intention and reacting appropriately to provide the required assistance in an opportune way. In this paper, we study the performance of superficial EMG intended to design a intent pattern recognition based on Artificial Neural Networks (ANN) trained by the Levenberg-Marquardt method. Experiments consisting in 231 EMG records corresponding to 13 lower limbs muscles from 21 healthy subjects were considered. The EMG signals were randomly divided into the following sets: 70 % for training, 15 % for validation and 15 % for evaluation. The ANN-based pattern recognition was evaluated sample per sample with the movement intention annotations (target) and after the traininig operation end, the performance was evaluated in relation to the events (number of steps). The results show an accuracy of 90,96% sample per sample and 94,88% for an based on events evaluation. These findings motivates the use of this methodology for the classification of the motion intention detection in subjects with pathologies in the lower limbs.