Real-time processing of electromyograms in an automated hand-forearm data collection and analysis system

Abstract

Handgrip contractions are a useful exercise for assessing muscle fatigue in the forearm musculature. Most conventional hand-forearm ergometer systems require the researcher to manually guide subject activity, collect subject data, and assess subject fatigue after it has occurred. Since post-processing tools are not standardized for this type of experiment, researchers resort to building their own tools. This process can make comparing results between research groups difficult. This thesis presents updates to a hand-forearm ergometer system that automate the control, data-acquisition, and data-analysis mechanisms. The automated system utilizes a LabVIEW virtual instrument as the system centerpiece; it provides the subject/researcher interfaces and coordinates data acquisition from both traditional and new sensors. The system also processes the hand-forearm data within the LabVIEW environment as the data are collected. This allows the researcher to better understand the onset of subject fatigue while an experiment is in progress. System upgrades relative to prior work include the addition of new parameters to the researcher display, a change in the subject display from a binary up-down display to a sliding bar for better control over subject grip state, and a software update from a simple data acquisition and display system to a real-time processing system. The toolset has proven to be a viable support resource for experimental studies performed in the Kansas State University Human Exercise Physiology Laboratory that target muscle fatigue in human forearms. Initial data acquired during these tests indicate the viability of the system to acquire consistent and physiologically meaningful data while providing a useable toolset for follow-on data analyses.