The electrical activity of skeletal muscle—the electromyogram (EMG)—is of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications, the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system (as evidenced in the surface EMG) and peripheral joints and muscles. This thesis work concentrated on experimental investigations of EMG-torque modeling. My contributions include: 1) continuing to evaluate the advantage of advanced EMG amplitude estimators, 2) studying system identification techniques (regularizing the least squares fit and increasing training data duration) to improve EMG-torque model performance, and 3) investigating the influence of joint angle on EMG-torque modeling. Results show that the advanced EMG amplitude estimator reduced the model error by 21%—71% compared to conventional estimators. Use of the regularized least squares fit with 52 seconds of training data reduced the model error by 20% compared to the least squares fit without regulation when using 26 seconds of training data. It is also demonstrated that the influence of joint angle can be modeled as a multiplicative factor in slowly force-varying and force-varying contractions at various, fixed angles. The performance of the models that account for the joint angle are not statistically different from a model that was trained at each angle separately and thus does not interpolate across angles. The EMG-torque models that account for joint angle and utilize advanced EMG amplitude estimation and system identification techniques achieved an error of 4.06±1.2% MVCF90 (i.e., error referenced to maximum voluntary contraction at 90° flexion), while models without using these advanced techniques and only accounting for a joint angle of 90° generated an error of 19.15±11.2% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) EMG-force modeling at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (2) Investigation of EMG bandwidth requirements for whitening for real-time applications of EMG whitening techniques [with WPI colleagues]. (3) Investigation of the ability of surface EMG to estimate joint torque at future times [with WPI colleagues]. (4) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA].

While conventional turbines have been extensively researched and tested, Tesla and boundary layer type turbines have not. In order to construct a dynamometer, thermodynamic flow apparatus and future turbines, we modeled the Tesla turbine using theoretical calculations and preliminary experiments. Thus a series of experiments were run to determine stall torque and maximum run speed for a known pressure range. This data was then applied to modeling formulas to estimate stall torque over an extended range of variables. The data were then used to design an appropriate dynamometer and airflow experiment. The model data also served to estimate various specifications and power output of the future turbine. An Obi Laser SSTG‐001 Tesla turbine was used in the experiments described. Experimental stall torque measurements were conducted in two stages. Shaft speed measurements were taken with an optical laser tachometer and Tesla turbine stall torque was measured using a spring force gauge. Two methods were chosen to model Tesla turbine stall torque: 1) flow over flat plate and 2) free vortex with a sink. A functional dynamometer and thermodynamic apparatus were constructed once the model was confirmed to be within the experimental uncertainty. Results of the experiments show that the experimental turbine at 65 PSI has a speed of approximately 27,000 RPM and a measured stall torque of 0.1279 N‐m. 65 PSI is an important data point because that data set is the cut‐off from laminar to turbulent flow. Thus at 65 PSI, a rejection of the null hypothesis for research question one with respect to the flow over flat plate method can be seen from the data, while the vortex model results in a failure to reject the null hypothesis. In conclusion, the experimental turbine was seen to have a laminar and a turbulent flow regime at different air pressures, rather than the assumed laminar flow regime. As a result of this model work, a new Tesla turbine of different dimensions was designed to adjust for flaws in the experimental turbine. The theoretical stall torque models were then applied to the new Tesla turbine design. Results of the models show that the vortex model sets the upper bound for theoretical stall torque for the new and the flat plate flow model sets the lower bound.