Electroencephalography (EEG) non-invasively records electrocortical activity and can be used to understand how the brain functions to control movements and walking. Studies have shown that electrocortical dynamics are coupled with the gait cycle and change when walking at different speeds. Thus, EEG signals likely contain information regarding walking speed that could potentially be used to predict walking speed using just EEG signals recorded during walking. The purpose of this study was to determine whether walking speed could be predicted from EEG recorded as subjects walked on a treadmill with a range of speeds (0.5 m/s, 0.75 m/s, 1.0 m/s, 1.25 m/s, and self-paced). We first applied spatial Independent Component Analysis (sICA) to reduce temporal dimensionality and then used current popular classification methods: Bagging, Boosting, Random Forest, Naïve Bayes, Logistic Regression, and Support Vector Machines with a linear and radial basis function kernel. We evaluated the precision, sensitivity, and specificity of each classifier. Logistic regression had the highest overall performance (76.6 +/- 13.9%), and had the highest precision (86.3 +/- 11.7%) and sensitivity (88.7 +/- 8.7%). The Support Vector Machine with a radial basis function kernel had the highest specificity (60.7 +/- 39.1%). These overall performance values are relatively good since the EEG data had only been high-pass filtered with a 1 Hz cutoff frequency and no extensive cleaning methods were performed. All of the classifiers had an overall performance of at least 68% except for the Support Vector Machine with a linear kernel, which had an overall performance of 55.4%. These results suggest that applying spatial Independent Component Analysis to reduce temporal dimensionality of EEG signals does not significantly impair the classification of walking speed using EEG and that walking speeds can be predicted from EEG data.

The objectives of this study were to (1) use kinetics from motion analysis and inverse dynamics to calculate the stress experienced by the ulnar collateral ligament (UCL) during a typical pitch cycle, (2) compare calculated maximum UCL pitching stresses to failure properties, and (3) investigate correlations between UCL stress with anthropometric and pitching biomechanical parameters. Prior motion analysis experiments of eighteen 10- to 11- year-old baseball pitchers throwing 10 fastballs were analyzed. Maximum internal elbow varus torques were calculated using inverse dynamics methods during a typical pitch cycle. Calculations used axial loading stress equations and maximum internal elbow varus torques to quantify the maximum UCL pitching stresses. UCL ultimate stresses and number of cycles to failure were calculated from prior studies with a scaling procedure to estimate youth participant values. The calculated maximum UCL pitching stresses were then compared to the estimated ultimate stresses using a paired t-test. The first major result of this study was that the maximum UCL pitching stresses were 33.83 MPa lower, on average, than the estimated ultimate stresses (p < 0.001). A second major result of this study was the estimated average number of cycles to failure of the UCL were 80,000+ higher, on average, than the maximum season (p < 0.001) and annual (p < 0.001) pitch counts. A third major result of this study was maximum UCL pitching stresses were significantly and positively correlated with pitch speeds, maximum shoulder external rotation torque, and maximum elbow varus torque. These results suggest 10- to 11- year-old pitchers are not likely to experience a UCL injury. The findings of this study are supported by clinical observations of elbow injuries in youth pitchers occurring primarily in other tissues.

Rotator cuff (RC) tears are the most common cause of shoulder disability, representing one of the highest days-away-from-work rates compared to other work-related injuries. Chronic, degenerative tears can cause pain, decreased range of motion, and weakness, with more than 50% of cases affecting individuals over 60 years of age. As Americans age, they remain active and contribute to the workforce longer than has been seen previously. Thus, the impact of RC pathology on activities of daily living and work activities is expected to grow. Previous work in the area of rotator cuff tear spans a number of scientific and clinical fields, but the results from each setting do not necessarily translate. Therefore, this necessitates coordinated, multi-faceted research into understanding how and why rotator cuff tendon tears initiate and progress. RC tears can be classified by their depth as either full or partial thickness tears, and previous clinical studies report a higher prevalence of partial thickness rotator cuff tears, though most research has focused on full thickness tears. Partial thickness rotator cuff tears are commonly asymptomatic, but may serve as an early timepoint that allows for improved, early clinical intervention. Therefore, the goal of this work was to develop a framework that collates information from clinical, cadaveric, simulation, and animal settings to quantify the changing mechanical environment surrounding partial rotator cuff tear and guide clinical assessment. In Chapter 2, using a seven degree of freedom glenohumeral testing system, we demonstrated 1) the effect of the rotator cuff muscle activation and 2) the role of negative intraarticular pressure during passive glenohumeral abduction. In Chapter 3, we utilize an adjustable material testing apparatus, biaxial tensile material testing of rotator cuff tendon specimens and material fitting to a hyperelastic, fiber-reinforced constitutive model to validate a specimen-specific finite element model of the rotator cuff. In Chapter 4, we formulate a procedure for evaluating partial rotator cuff tear patient motions that can be used as inputs to the validated finite element model. In Chapter 5, we develop an in-vivo small animal testing apparatus for evaluating the mechanical and biological response of tendon during cyclic loading. Ultimately this work serves as a foundation for a coordinated framework that takes partial rotator cuff tear patient information and provides the clinician with quantified rotator cuff tear progression risk. Futures studies that aim to achieve clinical utility will use this framework in conjunction with clinical insight to achieve clinical translation that reduce pain and loss of function due to rotator cuff tear. / 2023-01-26T00:00:00Z

Mobile brain/body imaging utilizes electroencephalography (EEG) to record brain activity during human walking in dynamic environments. Motion artifact from cable sway affects the quality of EEG signals collected from the scalp, masking contributions of synchronously firing neurons. Previous studies have explored cable sway-induced motion artifact, but only during vigorous exercise or controlled sinusoidal motion. Therefore, a need remains to further understand the underlying mechanisms of this artifact, as it may help with developing real-time mitigation methods, reducing reliance on offline signal processing. In this thesis, I aimed to show that controlled cable sway could produce specific motion artifact waveforms in a benchtop setup. I programmed a robotic arm to perform three different types of waveform motions - sinusoidal, square, and sawtooth - in two setups that had different levels of cable support: constrained and unconstrained. I used a novel dual-sided EEG electrode where one side of the electrode interfaces with the scalp to record traditional EEG signals while an outer-facing electrode interfaces with a conductive fabric cap to record isolated motion artifact and noise. Additionally, I placed the electrodes in a 3D-printed holder designed to position them between two layers of conductive fabric and eliminate any electrode movement, which has not been previously constrained. Lastly, I computed correlations between cable sway and bottom electrode EEG, cable sway and top electrode EEG, and top and bottom EEG. Correlations for all variable combinations were low, ranging from -0.122 +/- 0.223 to 0.058 +/- 0.238. Out of six correlation measure comparisons (three across testing setups and three across waveform motions), five did not show significant differences (p-values = 0.391 - 0.958). These results suggest that EEG motion artifact is not the result of just mechanical deformation of the cables but likely requires simultaneous movement of the electrode itself, altering the electrode-conductive surface interface dynamics.

This dissertation presents a study on enhancing surgical needles using a novel composite coating and investigates its impact on needle insertion mechanics. The aim is to create a less invasive needle insertion for a range of surgeries, including biopsy, thermal ablation, brachytherapy, and drug delivery. The composite coating, composed of Polydopamine (PDA), Polytetrafluoroethylene (PTFE), and Activated Carbon (C), overcomes the limitations of conventional needles by providing low friction and non-adhesive properties. As a result, it requires less insertion force and causes less tissue damage during needle insertion. In this research, the composite coating applied to a solid 18-gauge biopsy needle, equipped with a trocar tip, led to a notable reduction in insertion force, ranging from 30% to 49%. Furthermore, it exhibited an average improvement of 39% in minimizing tissue damage in bovine kidney tissues. This improved performance was observed with the composite coated needle as opposed to the bare one, attributable to a 56.9% reduction in the surface roughness RMS due to the coating.In order to further investigate the mechanics behind the improved performance of the coated needle, an analytical insertion force model was developed, taking into account Coulomb friction and viscoelastic forces, as well as cutting force. The Coulomb friction and viscoelastic forces were modeled by adapting the Karnopp model, and the cutting force model was formulated based on the premise of cutting forces being linear with insertion velocity. The insertion force model was evaluated using experiments performed on bovine kidney tissue. Based on the model prediction, it was determined that the composite coating  performed better due to a reduction in Coulomb friction force. Compared to the experimental data, the accuracy range of the model was determined to be between 6.5% and 17.1% for bovine kidney. A limitation of the model is that it does not replicate forces for each individual layer of tissue, but instead provides an average force estimation for heterogeneous tissue. The model was constructed based on the assumption of needle insertion occurring without any needle deflection. This dissertation provides critical insights into the field of surgical needles, demonstrating the considerable advantages of a novel composite coating. This innovation significantly lowers the insertion force and reduces tissue damage, opening the door to less invasive techniques for a myriad of surgical procedures. Despite the analytical model limitation, it is useful in surgeries preplanning, simulation, and robotic surgeries. Furthermore, the model has the potential to serve as a valuable tool for medical professionals, as it can furnish information on the location of the needle within the body from the predicted forces, in addition to the detection of variations in tissue stiffness. This information can assist in the accurate performance of medical procedures, leading to improved medical surgeries. / Mechanical Engineering

Intra-articular corticosteroid knee injections (ICIs) were used as a tool to determine the sensitivity of wearable inertial sensors and machine learning algorithms in identifying meaningful changes in gait patterns amidst day-to-day fluctuations in out-of-laboratory gait. Specifically, three overarching aims were proposed; I) Determine if three gait trials could define an everyday typical gait pattern, II) investigate if post-injection atypical strides are significantly different from pre-injection atypical strides and III) explore the relationship between changes in pain and atypical strides. Nine knee OA patients (7M/2F) were recruited from St. Joseph’s Healthcare Hamilton. Participants completed a total of four walking trials prior to the ICI and three following. Participants were fitted with two wearable sensors on each shank just below the knee, and one sensor on the lower back during every trial. Data from these sensors were processed to train and test a one-class support vector machine (OCSVM). Individual gait models were created based on three out of the four pre-injection trials. Each trained model was tested on a withheld pre-injection trial and three post-injection trials to determine the number of typical and atypical gait cycles. Self-reported pain was analyzed throughout the study and compared to the percent of atypical strides seen during each walk. It was found that three gait trials could not define a typical gait model and that post-injection atypical strides were not significantly different from with-held pre-injection atypical strides. Finally, large variations and fluctuations in self-reported pain were observed on a week-to-week basis, which were not significantly correlated to atypical strides observed. This study was the first to investigate the sensitivity of wearable inertial sensors and machine learning algorithms to detect changes in real-world gait patterns and provides foundational work for using wearable sensors to monitor and triage knee OA patients. / Thesis / Master of Science (MSc)

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