Analysis of electromyographic activity of ankle muscles on stable and unstable surfaces with eyes open and closed.

Introduction: Proprioceptive exercises are performed on a daily basis in physiotherapy with the use of different unstable platforms in order to improve joint stability using the mechanical and sensory properties of ligaments, joint capsule and integrated activity of the muscles surrounding the joint. Changes in the myoelectrical characteristics of the muscles during activity can be identified using surface electromyography (EMG), which provides important information on the behavior of muscles submitted to different types of load.

Objectives: The aim of the present study was to analyze the electromyographic activity of the tibialis anterior, tibialis posterior, peroneus longus, gastrocnemius lateralis and gastrocnemius medialis on stable and unstable surfaces with eyes open and closed.

Influence of heat on fatigue and electromyographic activity of the biceps brachii muscle.

Electromyography enables registering muscle activity during contraction and can identify muscle fatigue. In the present study, 30 volunteers between 18 and 30 years of age were submitted to an exertion 1 min of maximal voluntary isometric contraction. The electromyographic signal of the biceps brachii muscle and the strength of the flexor muscles of the elbow were determined before and after the administration of microwave diathermy in order to analyze the influence of heat over the strength of the elbow flexor muscles and fatigue of the biceps brachii.

ProRaman: a program to classify Raman spectra.

The program ProRaman, developed for the Matlab platform, provides an interactive and flexible graphic interface to develop efficient algorithms to classify Raman spectra into two or three different classes. A set of preprocessing algorithms to decrease the variable dimensionality and to extract the main features which improve the correct classification ratio was implemented. The implemented classification algorithms were based on the Mahalanobis distance and neural network.