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Therapeutic exercise has previously been shown to alter the static height of the medial longitudinal arch (MLA). It is still unknown, however, if these effects carry over into dynamic activities. PURPOSE: To determine if an 8-week foot strengthening exercise program increases static arch height and reduces vertical deformation of the MLA during mid-stance in running.METHODS: Thirty-four recreational runners (17 males, 17 females) have completed this ongoing study (age 24.06 ± 3.61 years, body mass 68.63 ± 12.95 kg, and height 173.34 ± 9.54 cm). To date, 22 subjects have been assigned to the control group (8 weeks of normal running) and 12 to the foot strengthening group (8 weeks of foot strengthening, along with normal running). Static arch height (SAH) and dynamic arch drop (DAD) were measured at baseline and following the 8-week intervention using Vicon motion analysis. Reflective markers were placed on the proximal and distal ends of the 1st and 5th metatarsals. These 4 markers were recorded in static double leg stance to estimate SAH, and in single leg mid-stance to give a measure of DAD during treadmill running at a self-selected pace. Ten-second trials were recorded at minutes 3 and 4 during running and DAD was evaluated for right and left feet by comparing arch height in mid-stance to the SAH. Following the intervention, data for SAH and DAD were compared across time points and statistical analysis performed to identify differences in the amount of change in SAH and DAD between groups. RESULTS: There was no difference noted in DAD between the groups as a whole, but the change in DAD from baseline to the end of week 8 was statistically significant for those in the foot strengthening group with an initial DAD of ≥ 3.80 mm (p < .028). There was also a statistically significant increase in SAH in the foot strengthening group compared to the control group (p = .013). CONCLUSIONS: These preliminary data suggest that the foot strengthening intervention was effective in increasing SAH compared to the control group. The intervention was most effective at decreasing DAD in those with the largest amount of DAD at baseline. At this time it is unknown whether this decrease in arch drop is associated with performance benefits or decreased injury risk in the recreational runner, and further research is needed to determine the clinical significance of these findings.

Strength and tone of the pelvic floor muscles are thought to play an important role in pelvic floor disorders, and are key outcomes monitored in pelvic floor rehabilitation, yet the standard approach to measuring these outcomes is through subjective assessment using manual palpation. While intravaginal dynamometers (IVD) have been designed to measure these characteristics in research settings, most are not sufficient to withstand the rigors of clinical use. This work presents the bench validation and subsequent updated design of a new mechanical constant speed mechanism for future inclusion in an IVD. Opening speeds of the original mechanism were validated with and without external loading using video analysis. The bench validation showed that the speed of arm opening was lower than the ranges specified for clinical use and was influenced by external loading. The mechanism was updated, and the bench test was repeated. The updated mechanism was found to provide output speeds that are within the ranges required clinically and were minimally impacted by external loading. The next step of this work is to reduce the size of the mechanism and improve output speeds to allow it to be assembled into a clinical prototype IVD.

In the sport of track and field, runners excel at their events due not only to physiological characteristics but aspects in their form. Characteristics in form help runners achieve the goal in completing their event in the least amount of time possible. For sprinters, this is done by having a shorter swing phase and ground time along with greater power and a longer stride length. Distance runners accomplish the goal of quicker speeds by balancing it with running economy by spending greater time on the ground with shorter stride lengths compared to those in shorter distance running events. Middle distance runners must find a balance between power and running economy for greater success in the 800 meter and 1600 meter runs. If these characteristics are true for runners while competing in their event, would they also be seen at speeds slower and faster than what they compete at? Purpose: This study was conducted to determine if sprinters, middle distance runners, and distance runners running at the same speeds would exhibit different characteristics in their form which aid them in their events. Methods: Thirty female Division I collegiate runners participated in this study. Runners were separated into categories based on the events they were currently training in: 10 sprinters, 10 middle distance runners, and 10 distance runners. All participants were asked to run for twenty two steps at 3.17 m/s (8:27 min/mile), 3.58 m/s (7:30 min/mile), 4.11 m/s (6.31 min/mile), 4.87 m/s (5:30 min/mile), and 5.95 m/s (4:30 min/mile) pace. Motion analysis was captured at each speed recording knee angles, ground time, center of mass separation, and stride length at 240 Hz. Data was then processed using ANOVA and a Tukey post hoc analysis. Results: Significant differences (p < .05) occurred between distance runners and the groups of middle distance runners and sprinters in knee range, ground time, center of mass separation, and stride length while running at the same speed for all of the five speeds. All groups displayed similar liner slopes as speeds increased with no interactions occurring between groups. As the speed increased, all three groups decreased in knee range measurements and ground time measurements. Increases in speed displayed and increase in center of mass separation and stride length among all three groups. Conclusion: While running at the same speeds, runners exhibit specific characteristics in their form that benefit them in their event. These are even seen in speeds that are faster or slower that what the athletes are used to training at or competing at. In addition, middle distance runners display aspects of form that are between distance runners and sprinters in all variables. By understanding these differences, coaches and athletes can analyze current performance and make needed adjustments.

Sprinting on a curve is significantly slower than on a straightaway. Although the dimensions vary from track to track, indoor track curves are among the tightest curves that athletes will sprint at maximal speed. Previous studies have provided theories for how speed attenuation occurs when running on a curve. Yet, no previous research has determined how the variability of indoor track curve radii affects trained sprinters at maximal speeds. Purpose: To determine the differences in running speeds, ground time (GT), and medio-lateral (ML) impulse, with different indoor track radii. A secondary purpose was to understand the between-leg differences in GT and ML impulse during maximal sprinting on a curve. Methods: 10 male intercollegiate sprinters performed 45-m maximal sprints on a straightaway, 15-m track curve and 21-m track curve. A force platform embedded under an indoor track surface measured ground reaction forces while timing lights measured running speed. Analysis: A mixed models analysis of variance blocking on subjects was performed testing the main effects of the track curve on sprinting speed, GT and ML impulse (p<0.01). Results: Sprinting speed was significantly slower when running on a curve. GT increased for inside leg on both curved path conditions compared to straight. ML impulses increased as the radius of the track curve decreased. Discussion: If a 200m race were performed on both our track curves, the track with 21m curve would be 0.12s faster than the track with the 15m curve. GT and ML impulse results support leading explanations that the inside leg is the limiting factor during curve running. Tighter track curves require greater ML forces, but for a shorter period of time compared to larger track turns. Coaches and athletes should consider the radius of the track curve as they prepare for training and performance and consider injury risk. The speed differences observed due to the track curve radius may provide the first step to understanding how the radius of the indoor track curve affects sprinting speed and ultimately, performance.

The purpose of this study was to describe kinematic changes that occur during an actual marathon. We hypothesized that (1) certain running kinematic measures would change between miles 5 and 25 of a marathon and (2) fast runners would demonstrate smaller changes than slow runners. Subjects (n = 179) were selected according to finish time (Range = 2:20:47 to 5:30:10). Two high-speed cameras were used to measure sagittal-plane kinematics at miles 5 and 25 of the marathon. The dependent variables were stride length, ground time, peak knee flexion during support and swing, and peak hip flexion and extension during swing. Two-tailed paired t-tests were used to compare dependent variables between miles 5 and 25 for all subjects, and regression analyses were used to determine whether faster runners exhibited smaller changes (between miles 5 and 25) than slower runners. For all runners, every dependent variable changed significantly between miles 5 and 25 (p < 0.001). Stride length increased 1.3%, ground time increased 13.1%, peak knee flexion during support decreased 3.2%, and peak hip extension, knee flexion, and hip flexion during swing decreased 27.9%, increased 4.3%, and increased 7.4%, respectively (p<0.001). Among these significant changes, all runners generally changed the same from miles 5 to 25 except that fast runners decreased peak knee flexion during support less than the slow runners (p < 0.002). We believe these kinematic changes were an attempt by all runners (fast and slow) to decrease energy expenditure and enhance performance at the late stage of the race. The fact that fast runners maintained knee flexion during support more consistently might be due to their condition on the race day. Strengthening of knee extensor muscles may facilitate increased knee flexion during support throughout a marathon.

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The overarching goal of this work was to develop novel data collection and analysismethods to better understand how soldier burden affects the soldier survivability tradespace (i.e.,performance, musculoskeletal health, and susceptibility to enemy action). To achieve this goal,three studies were completed: 1) a mobile inertial measurement unit (IMU) suit was validatedagainst an optical motion capture (OPT) system; 2) data from the IMU suit was used to develop aframework for morphing movement patterns to represent intermediary body-borne load massesand personal characteristics; and 3) a single IMU was used to develop a human activity recognitionalgorithm and calculate tradespace metrics.In study one, a whole-body IMU suit (MVN Link, Xsens, Netherlands) was validatedagainst an OPT system (Vantage V5, Vicon, United Kingdom) for military-based movementsusing the root mean squared error (RMSE) of joint angles and Pearson correlation coefficients ofprincipal component (PC) scores. During a standard implementation (i.e., using differentbiomechanical models and not attempting to align them; VOPT vs. XIMU), average RMSE valuesacross all tasks were less than 9° for the lower limbs but up to 40.5° for the upper limbs. Whenusing the same biomechanical model and applying an alignment procedure (VOPT vs. VIMU-CAL),RMSE values decreased to an average of 2.5º and 17.5º for the lower and upper limbs, respectively.Of the 48 retained PCs, 38 (79%) had scores with a high or very high positive correlation (> +0.70)between the OPT and IMU systems, 15 (31%) of which had scores with a very high correlation (>+0.90). The average Pearson correlation coefficient was 0.81 (SD = 0.14). Given these results, theIMU system was deemed appropriate for collecting military-based movement patterns.In study two, principal component analysis (PCA) and linear discriminant analysis (LDA)were used to generate whole-body morphable movement patterns to represent intermediary body-ixborne loads and personal characteristics (sex, body mass, military experience). Reconstructedmovements were used for animation, musculoskeletal modelling, exposure time calculations, andsusceptibility calculations; all calculated values were comparable to previous research. Thisproject displayed that a relatively small representative dataset can be used to simulate the changein whole-body movement patterns caused by many different body-borne loads and personalcharacteristics not originally collected. By implementing this framework, defence scientists canreduce the amount and complexity of data collections needed to better understand the impact onthe survivability tradespace caused by all types of soldier burden.Study three focused on developing a deployable method for calculating tradespace metricsin the field. Three deep neural network (DNN) architectures were trained to identify eleven classlabels using data from a single IMU on the upper back. Data were collected during an indoorlaboratory-based protocol and an outdoor simulated two-person section attack. The predictionsmade by the DNNs were processed through a two-step logical algorithm to apply real-worldconstraints and expand the predictions to 19 class labels. The deep convolutional long short-termneural network architecture outperformed the convolutional neural network and fully-connectedneural network for all three approaches: indoor only, section attack only, and general. Movementswere identified with a high degree of accuracy (> 87% for accuracy and weighted F1-score), andtradespace metrics were calculated within 0.17 seconds, 0.21 shots, and 1.25% susceptibilitycompared to the tradespace metrics calculated from the ground truth labels.Overall, the data-driven methods developed throughout this dissertation can be used bydefence scientists and military leaders to improve the understanding of the survivabilitytradespace, which has the potential to improve the quality of life of soldiers, making them more fitand ready to fight, thus increasing the likelihood of mission success.