Master of Science / Department of Electrical and Computer Engineering / Steven Warren / 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.

Master of Science / Department of Electrical & Computer Engineering / Steve Warren / Handgrip contractions are a standard exercise modality to evaluate muscular system performance. Most conventional ergometer systems that collect handgrip contraction data are manually controlled, placing a burden on the researcher to guide subject activity while recording the resultant data. Further, post-processing tools for this type of experiment are not standardized within the domain, which requires investigators to process their data with multiple tool sets and often create custom tool sets for that purpose. This can make experimental data difficult to compare and correlate, even within the same research group. This thesis presents updates to a hand-forearm ergometer system that automate the control and data-acquisition processes as well as provide a tool set to post process hand contraction data. 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 tool set also incorporates a collection of MATLAB scripts that allow the investigator to post process these data in a standard way, such as automating the processes of noise floor removal, burst start/stop time identification, and mean/median frequency calculation in electromyograms (EMGs). The tool set has proven to be a viable support resource for experimental studies performed by the Kansas State University Human Exercise Physiology lab 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 usable tool set for follow-on data analyses.