Discrimination of Finger Movements Using Surface Electromyogram

Adhiti gupta, Dr. Hardeep S. Ryait


In this paper study on the anatomy of muscles of the forearm has been identified for finger movements by the researchers. The fingers of the hand are movable in four direction flexion (bending), extension (straightening), abduction (moving sideways from the body) and adduction (moving sideways towards the body). The muscles that flex the fingers primarily flexor digitorum superficialis and flexor digitorum profundus are located in the palmar aspect of the forearm. The addition of fingers movement would enhance the quality of life of physically disabled people in order for them to participate in the cultural events such as, playing sports or musical instruments, finger motion are essential. The Surface Electromyogram (SEMG) signal was a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. In the present research the SEMG signals were recorded non-invasively by placing surface electrodes at the forearm muscles (flexor digitorum superficialis and extensor digitorum superficialis) and for two operations (back side near elbow and front mid side) of forearm, for two channels, for the different movements of finger. For the acquisition of SEMG signals the SEMG amplifier was built, that consisted of differential amplifier and non-inverting amplifier were designed by using LM-324. The signals were carried out by bipolar electrodes and then transmitted to the amplifier. Both amplifiers were designed by using LM-324 chip. The SEMG signals were acquired from SEMG amplifier and then signal was transmitted to the interfacing device cRIO (Compact reconfigure input/output) and at last the interfacing device sends SEMG signals to the Lab View acquisition circuit for storing. After acquiring data from six subjects analysis was done using the selected parameters (RMS, Standard deviation, Variance, Min and Max). Further analysis was done on Variance-Covariance matrix for the different movements of fingers for two channels. The purpose of this research was to illustrate the methodology of SEMG signal analysis to provide efficient and effective way of understanding the signals and its nature. From the research it was observed that the variance-covariance matrix gave the best analysis of SEMG signals for finger movements. The channel 1 represented the dominance of variance in opening of hand with respect to thumb, index finger, middle finger, ring finger and little finger.The dominance of variance was maximum in middle finger and minimum in little finger. The ring finger has less dominance with respect to thumb. In channel 2 all the fingers has less dominance of variance. The dominance of covariance in opening of hand with thumb, index finger, middle finger, ring finger and little finger was also maximum in middle finger and minimum in little finger in channel 1.In channel 2 the covariance has less variation with all fingers. The discrimination rate could reach more than 90%.Thus study proves an alternative procedure in finger movements discrimination. In the future study the work must be extended for multisite movement analysis. Further studied could be extended to SEMG based frequency approach along with the root mean square value for better analysis.

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