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.