Alexandre Balbinot

Wireless Network for Measurement of Whole-Body Vibration

This article presents the development of a system integrated to a ZigBee network to measure whole-body vibration. The developed system allows distinguishing human vibrations of almost 400Hz in three axes with acceleration of almost 50g. The tests conducted in the study ensured the correct functioning of the system for the projects purpose.

A Neuro-Fuzzy System for Characterization of Arm Movements

The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).

Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control

The myoelectric signal is a sign of control of the human body that contains the information of the users intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86–98% for three subjects.

Decoding arm movements by myoeletric signals and artificial neural networks

The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (EMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68–88%.

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brains normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

A new crank arm-based load cell for the 3D analysis of the force applied by a cyclist.

This report describes a new crank arm-based force platform designed to evaluate the three-dimensional force applied to the pedals by cyclists in real conditions. The force platform was designed to be fitted on a conventional competition bicycle crankset while data is transmitted wirelessly through a BluetoothTM module and also stored on a SD card. A 3D solid model is created in the SolidWorks (Dassault Systèmes SOLIDWORKS Corp.) to analyze the static and dynamic characteristics of the crank arm by using the finite elements technique. Each crankset arm is used as a load cell based on strain gauges configured as three Wheatstone bridges. The signals are conditioned on a printed circuit board attached directly to the structure. The load cell showed a maximum nonlinearity error between 0.36% and 0.61% and a maximum uncertainty of 2.3% referred to the sensitivity of each channel. A roller trainer equipped with an optical encoder was also developed, allowing the measurement of the wheels instantaneous velocity.

A study of the Naive Bayes classifier for analyzing imaginary movement EEG signals using the Periodogram as spectral estimator

This study aims at applying algorithms for the spectral estimation and the classification of EEG signals during imaginary movements. Accordingly, it was used a database created by Graz University of Technology for the BCI Competition II. The database was created through an experiment in which an individual was asked to imagine the movement of her right or left hand while the EEG signal reading (Electroencephalogram) was being performed directly on the scalp by using three bipolar electrodes. In order to analyze the EEG signals for spectral estimation, it was used the Periodogram method computed in three different frequencies in μ-rythm (8 a 13Hz) and β-rythm (14 a 25Hz) frequencies to generate the Naive Bayes classifier entries. This database enabled to obtain a hit rate higher than 80%, which is consistent with results from the literature. Systems based on Brain-Computer Interface (BCI) can appropriate these algorithms to trigger and control devices through mental intentions only.

Development of a Brain-Computer Interface system based on sensorimotor rhythms

In this work we developed a Brain-Computer Interface system using brain signals from somatosensory cortex. As stimulus, arrows were shown to the right and left to five subjects, who perform imaginary hand movement tasks to the side indicated by the arrow. Subject-specific parameters were selected using DSLVQ (Distinctive Sensitive Learning Vector Quantization) and Lateralization Index (LI), in order to extract the most relevant features, resulting in a higher hit rate in the classification stage. Online classification was performed with Linear Discriminant Analysis resulting in hit rates between 81 and 92.1%.

Dynamic torque measurement for automotive application

This work presents a system for the measurement and monitoring of the torque transmitted to the wheel of a moving vehicle. The transducer was built on the aluminum alloy wheel and the electronic system installed right on the center of the wheel assembly. The whole system spins with the tire, commanded by the power sent by the engine. The designed beam load cell was milled on an aluminum radial spoke and strain gauges were attached to the points of maximum deformation. The signal from the strain gauge bridge, was amplified and filtered. Then it was sent to an analog to digital converter built on a wireless ZigBee transmission module. The receptor ZigBee module was connected to a notebook inside the vehicle. A static calibration has been conducted with calibration weights and the results show features like linearity, sensibility and noise rejection. Finally, preliminary dynamic experiments run on a flat road, have shown so far, that the device is able of measuring and monitoring the direct torque developed on the vehicles wheel during real conditions on the road.

Support vectors machine classification of surface electromyography for non-invasive naturally controlled hand prostheses

The scientific researches in human rehabilitation techniques have continually evolved to offer again the mobility and freedom lost to disability. Many systems managed by myoelectric signals intended to mimic the movement of the human arm still have results considered partial, which makes it subject of many researches. The use of Natural Interfaces Signal Processing methods makes possible to design systems capable of offering prosthesis in a more natural and intuitive way. This paper presents a study investigating the use of forearm surface electromyography (sEMG) signals for classification of specific movements of hand using 12 sEMG channels and support vector machine (SVM). The system acquired the sEMG signal using a virtual model as a visual stimulus in order to demonstrate to the volunteer the hand movements which must be replicated by them. The Root Mean Square (RMS) value feature is extracted of the signal and it serves as input data for the classification with SVM. The classification stage used three types of kernel functions (linear, polynomial, radial basis) for comparison of the results. The average accuracy reached for the classification of seventeen distinct movements of 83.7% was achieved using the SVM linear classifier, 80.8% was achieved using the SVM polynomial classifier and 85.1% was achieved using the SVM radial basis classifier.