A new crank arm based load cell, with built-in conditioning circuit and strain gages, to measure the components of the force applied by a cyclist.

This report describes the development of a force platform based on instrumented load cells with built-in conditioning circuit and strain gages to measure and acquire the components of the force that is applied to the bike crank arm during pedaling in real conditions, and save them on a SD Card. To accomplish that, a complete new crank arm 3D solid model was developed in the SolidWorks, with dimensions equivalent to a commercial crank set and compatible with a conventional road bike, but with a compartment to support all the electronics necessary to measure 3 components of the force applied to the pedal during pedaling. After that, a 6082 T6 Aluminum Crankset based on the solid model was made and instrumented with three Wheatstone bridges each.

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.

Novel method to characterize upper-limb movements based on paraconsistent logic and myoelectric signals.

This paper presents a novel method that investigates the use of Paraconsistent Artificial Neural Network (PANN) and upper-limb electromyography signals for classification of movements, due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The preliminary study presents promising results in terms of processing time and accuracy. The average classification accuracy for the developed paraconsistent logic method was 76,0±9,1% for 17 distinguish movements and a classification average processing time of 14 ms per movement.

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 user's 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.