Theses and Dissertations at Montana State University (MSU)
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Item Practicality and usability of high-density surface electromyography for lower limb prosthesis control(Montana State University - Bozeman, College of Engineering, 2022) Christensen, Fred Wallace; Chairperson, Graduate Committee: Corey PewSurface electromyography (sEMG) presents a pathway for prosthesis control but is prone to excess noise and signal corruption due to displacement. High Density Surface Electromyography (HDsEMG), which covers the same area as Traditional sEMG with multiple electrode channels as opposed to one channel, presents a way to overcome these challenges. Seven healthy participants were recruited and performed several activities of daily living with both Traditional sEMG and HDsEMG sensors on their Rectus Femoris, Biceps Femoris, Vastus Lateralis, and Semitendinosus muscles. These sensors were placed in both optimal locations over the muscle belly and in a location 1 cm distally from that optimal placement to simulate sensor displacement with use. From the data collected, four signals were created: a Traditional sEMG signal, the single HDsEMG signal with the highest signal-to-noise ratio (SNR) (Best Signal), a time mean of all HDsEMG signals (Composite Signal), and a time mean of all HDsEMG signals with SNR values greater than 2 dB (Threshold Signal). All signals' values for SNR, root-mean-squared means (RMS), DP ratio, and Omega ratio were compared in both optimal and displaced conditions. Phase lag and power domain similarity were used to assess response to displacement. Threshold mean and straight mean signals were identical in most values. The best signal displayed highest SNR, with the composite signal displaying second highest, and sEMG displaying lowest. These differences were more pronounced in extensor muscles in activities that involved large amounts of knee movements. sEMG signals displayed higher relative RMS values, as well as higher DP values. sEMG displayed statistically higher, but numerically similar Omega values. sEMG displayed a greater agreement between optimal and displaced signals in the frequency domain. Similarity was more dependent on activity type than signal type. Phase lag was determined to not be relevant. HDsEMG was proved to have potential for improved prosthesis control.Item Change in finger force production and muscle activation in the forearms of rock climbers during treadwall climbing(Montana State University - Bozeman, College of Education, Health & Human Development, 2018) Ferrara, Philip Frank, III; Chairperson, Graduate Committee: John G. Seifert; John G. Seifert, Mary P. Miles and James Becker were co-authors of the article, 'Change in finger force production and muscle activation in the forearms of climbers during treadwall climbing' submitted to the journal 'Journal of Sport Sciences' which is contained within this thesis.Rock climbing is a multi-faceted sport requiring finger flexor strength and endurance. Sustained isometric contractions lead to the build-up of metabolic byproducts that fatigue the finger flexors, however the effect of climbing ability on muscular fatigue is not fully understood. The purpose of the present study is to investigate the effects of rock climbing ability on time to fatigue (TTF), relative finger force production (REL FP), change in FP (DeltaFP), and changes in muscle activity during bouts of climbing on a treadwall. Eight advanced (6 male, 2 female: 29.3 + or = 4.7 yrs, 69.1 + or = 6.9 kg, years experience: 11.1 + or = 5.2) and seven novice (5 male, 2 female: 21 + or = 2.3 yrs, 67.6 + or = 3.8 kg, years experience: 3.0 + or = 2.6) subjects participated. Subjects warmed-up on the treadwall and mounted force tranducer. Electromyographic (EMG) electrodes were placed over the flexor digitorum superficialis (FDS), biceps brachii and triceps brachii muscle to measure motor unit action potentials. Root mean square (RMS) and median frequency (MF) were analyzed from EMG data. Subjects performed a pre-exercise, 20-second maximal voluntary isometric contraction (MVIC PRE) with the fingertips of the dominant hand (DH) and non- dominant hand (NDH). The climbing protocol consisted of climbing for 5-minute intervals. Subjects performed another MVIC after each interval. EMG and force data were recorded during MVICs. A total of six intervals were performed, or until failure. Group comparisons were made at the 5th interval (MVIC POST). Climbing ability and handedness were analyzed using a 2x2 mixed ANOVA with repeated measures (alpha level < 0.05). Significant group differences were observed for TTF, REL FP, and percent DeltaFP and FDS DeltaMF. Advanced climbers' average REL FP during MVIC PRE was 5.6 + or = 1.4 N/kg BW and 5.2 + or = 1.6 N/kg from the DH and NDH, respectively. Novice REL FP was 3.1 + or = 0.8 N/kg BW and 3.1 + or = 1.0 N/kg. Novices DeltaFP decreased 30.8 + or = 16.0% and 24.9 + or = 18.6%, advanced climbers experienced no change. Advanced MF increased 4.8 + or = 25.9% and 7.7 + or = 18.8%, novice MF decreased 22.7 + or = 6.5% and 12.6 + or = 15.5%. In conclusion, advanced climbers demonstrated a resistance to climbing-specific fatigue during bouts of treadwall climbing.Item Joint moment estimation from electromyography of patients with osteoarthritis(Montana State University - Bozeman, College of Education, Health & Human Development, 2007) O'Keefe, Kathryn Bernadine; Chairperson, Graduate Committee: Michael E. Hahn.Biomechanical gait analysis may be used to determine treatment options, evaluate the success of rehabilitation programs or post-surgery recuperation, and provide insight for surgical planning, including functional outcomes for patients. However, gait analysis requires expensive equipment - a limiting factor for many clinical settings. One alternative that has been examined is the utilization of an artificial neural network (ANN) to model nonlinear relationships of gait. Researchers have shown initial success in ANN predictions of pathological conditions in gait as well as modeling other parameters. The purpose of this study was to evaluate the performance of a previously developed three layer feed-forward ANN model at estimating ankle, knee and hip joint moments for subjects with osteoarthrits (OA) from surface electromyography (EMG) signals. The broader purpose was to further validate the use of the ANN model as an alternative, less expensive method to traditional gait analysis. Eighteen subjects (13 female, 5 male) with physician diagnosed OA participated in this study. Each subject completed a full gait analysis session.