Knowledge Based database of arm-muscle and activity characterization during load pull exercise using Diagnostic Electromyography (D-EMG) Signal.

Abstract

In this paper, Diagnostic Electromyography (D-EMG) signal interpretation of human arm towards characterization of arm-muscle interaction during various arm movements has been discussed. EMG signals from four important arm muscle (i.e., Bicepsbracci, Tricepsbracci, brachioradialis, and lateral deltoids) are recorded clinically during five different arm movements (i.e., Extension of the forearm, flexion of elbow joint, pronation of forearm, shoulder abduction, and Wrist flexor stretch) under load condition (a load of 2 Kg & 4 Kg maintained during experimental arm movement), the recorded D-EMG signals are properly enveloped within a range of 5–100 Hz and quantized within a proper sampling frequency range to produce a knowledge-based database of muscle activity. In addition, correlation of muscle activity and Power spectral density (PSD) analysis has been carried out towards muscle process discriminating during various arm actions.

Keywords

• knowledge-based database
• diagnostic electromyography(demg)
• arm movement
• muscle activity
• envelope detection
• sampling
• correlation of muscle activity
• power spectral density (psd)
• big data