Kinesiology
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Item Gender Effects on Knee Loading and Prediction of Knee Loads Using Instrumented Insoles and Machine Learning(2024) Snyder, Samantha Jane; Miller, Ross H.; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Women are more likely to experience knee osteoarthritis as compared to men, but the underlying mechanisms behind this disparity are unclear. Greater knee loads, knee adduction moment, knee flexion moment, and medial joint contact force, are linked to severity and progression of knee osteoarthritis. However, it is unknown if greater knee loads in healthy, young women during activities of daily living (sit-to-stand, stand-to-sit, walking and running) can partially explain the higher prevalence of knee osteoarthritis rates in women. Although previous research showed no significant differences in peak knee adduction moment and knee flexion moment between men and women, differences in peak medial joint contact force are largely unexplored. Women also tend to take shorter steps and run slower than men. It is unknown if these differences may result in greater cumulative knee loading per unit distance traveled as compared to men. Furthermore, knee loading measurement is typically confined to a gait laboratory, yet the knee is subjected to large cyclical loads throughout daily life. The combination of machine learning techniques and wearable sensors has been shown to improve accessibility of biomechanical measurements without compromising accuracy. Therefore, the goal of this dissertation is to develop a framework for measuring these risk factors using machine learning and novel instrumented insoles, and to investigate differences in peak and cumulative per unit distance traveled knee loads between young, healthy men and women. In study 1 we developed instrumented insoles and examined insole reliability and validity. In study 2, we estimated knee loads for most activities with strong correlation coefficients and low to moderate mean absolute errors. In study 3, we found peak medial joint contact force was not significantly different across activities for men and women. Similarly, in study 4, we found no significant difference between men and women in knee loads per unit distance traveled during walking and running. These findings suggest biomechanical mechanisms alone cannot explain the disproportionate rate of knee osteoarthritis in women. However, in future research, the developed knee loading prediction models can help quantify daily knee loads and aid in reducing knee osteoarthritis risk in both men and women.Item LOWER LIMB ASYMMETRY AND LOADING IN INDIVIDUALS WITH UNILATERAL TRANSFEMORAL AMPUTATIONS WITH A LIFETIME OF OSSEOINTEGRATED PROSTHESIS USE(2023) Burnett, Jenna K; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Individuals with transfemoral amputation commonly develop chronic health problems due to decreased physical activity as a result of the missing musculature and tissue on the amputated side, and the poor imitation of the intact limb provided by the prosthesis. In addition, the indirect and semi-rigid connection of the socket to the body may increase interlimb asymmetries, as well as lead to pain and discomfort on the residual limb. Recent innovations have introduced a bone-anchored or osseointegrated (OI) implant which connects the prosthesis to the skeleton, and removes most of the socket related pain and discomfort complaints, as well as providing a rigid connection which may reduce the interlimb asymmetries. However, the direct bone and prosthesis connection may also introduce longitudinal bone health concerns due to the repetitive loads during walking. This dissertation investigated the effect of walking speed on the loads placed on the lower limbs of 11 individuals who use an OI prosthesis at 3 different anatomical levels, including the whole limb through interlimb ground reaction force, the joints through interlimb joint kinematics and kinetics, and finally the residual limb bone through implant input forces, finite element analysis of bone strain, and the probability of bone injury with a simulated lifetime of use.In study 1, the interlimb ground reaction force asymmetries were found to be moderate to large at all walking speeds, and to have a general increase as individuals walked faster, indicating there is an intact limb reliance strategy which may be used to compensate for the limitations of the amputated limb. Similarly, in study 2, the interlimb joint kinematics and kinetics were found to have moderate to large asymmetries at each joint level, with a general increase in asymmetry at faster walking, with this increase largely due to limitations within the prothesis. In study 3, the abutment force decreased in magnitude with walking speed, but the peak strain on the bone, and the probability of injury was greater for the preferred speed and fast speed walking when compared to slow speed walking. However, the overall probability of injury was low for all speeds, indicating the ability of the bone to repair and adapt with sustained loading likely provides effective protection over a lifetime of simulated OI prothesis use. The findings of this dissertation suggest that the more rigid connection afforded by the OI implant cannot fully remove the interlimb asymmetries which occur as a result of the poor imitation of the intact limb provided by the prosthesis and prosthesis components, but that there is minimal risk to the bone due to a lifetime of sustained walking with an OI prosthesis as a result the inherent ability of the bone to repair and adapt to variable loads over time. Therefore, while an OI prosthesis may not fully mitigate the interlimb asymmetries which occur as a result of the prosthesis limitations, individuals who use an OI prosthesis may feel confident that there is minimal longitudinal risk to the bone as a result of walking over their lifetime.Item MODELING THE MECHANICAL CONSEQUENCES OF PREGNANCY ON KNEE JOINT LOADING AND FUTURE KNEE HEALTH(2023) Bell, Elizabeth M; Miller, Ross H; Shim, Jae K; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Clinical evidence suggests that experiencing pregnancy increases a woman’s risk of knee osteoarthritis, a painful and mobility limiting disease that results from cartilage deterioration. While understanding the underlying causes and the association with pregnancy is complex, the mechanical load on cartilage during walking appears to be important to the initiation and progression of the disease, especially if walking mechanics are abnormal. Pregnancy involves various changes in mechanical factors like mass, center of mass, and joint laxity which are known to progressively change walking mechanics throughout gestation. However, it is unknown if mechanical changes associated with pregnancy, which may be substantial in magnitude but may be limited in duration, can explain the osteoarthritis risk since osteoarthritis is diagnosed later in life. Given that women typically experience pregnancy early in their lifetime and will need healthy knees for decades after they become mothers, this research aimed to model the mechanical consequences of pregnancy on knee joint loading and knee joint health over the lifetime. Specifically, this dissertation sought to (i) determine how pregnancy influences variables like resultant knee joint kinetics, which more directly indicate the load on cartilage over a range of walking speeds (ii) estimate the impact of pregnancy on internal knee joint forces and tibiofemoral cartilage load during walking and (iii) evaluate the isolated effect of altered loading experienced during pregnancy on cartilage degeneration and the risk of knee osteoarthritis throughout a woman's lifetime. Results suggest that (i) 3D knee joint moments over a range of walking speeds are greater in pregnant vs. non-pregnant individuals and knee adduction moments are altered as pregnant women walk faster. Similarly, pregnant women experience greater total knee joint loading and greater medial knee joint loading which results in additional and altered peak strain on knee cartilage with greater walking speed. Finally, the elevated and altered compressive load experienced over one or more pregnancies resulted in a greater cartilage failure probability, with differential effects when women experience multiple pregnancies later in their lifetime. These findings support the notion that the mechanical factors associated with pregnancy significantly alter knee joint loading and mechanical changes may, in part, contribute to the known association between pregnancy and risk for knee osteoarthritis risk over a woman’s lifetime. Further, present-day American mothers who are conceiving at later stages of life compared to previous generations may be more susceptible to knee osteoarthritis. Future investigations are needed to explore effects postpartum and for populations beyond healthy, active pregnant women. Further research could also investigate if biomechanical adjustments could be used as potential interventions to lessen knee joint loading and potentially decrease the risk of knee osteoarthritis among this population.Item Estimating Biomechanical Risk Factors of Knee Osteoarthritis in Gait Using Instrumented Shoe Insole and Deep Learning Approaches(2021) Snyder, Samantha Jane; Miller, Ross; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to implement an alternative to the cost-ineffective and time consuming current inverse dynamics approaches and predict knee adduction moments, a known predictor of knee osteoarthritis, through deep learning neural networks and a custom instrumented insole. Feed-forward, convolutional, and recurrent neural networks are applied to the data extracted from five piezo-resistive force sensors attached to the insole of a shoe. All models predicted knee adduction moment variables during walking with high correlation coefficients, greater than 0.72, and low root mean squared errors, ranging from 0.6-1.2%. The convolutional neural network is the most accurate predictor followed by the recurrent and feed-forward neural networks. These findings and the methods presented in the current study are expected to facilitate a cost-effective clinical analysis of knee adduction moments and to simplify future research studying the relationship between knee adduction moments and knee osteoarthritis.Item THE EFFECTS OF TRAINING HABITS ON CUMULATIVE LOAD AND TIBIAL STRESS FRACTURE INJURY RISK IN RUNNERS(2020) Hunter, Jessica G.; Miller, Ross H.; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Running for exercise is beneficial for preventing chronic diseases, but the incidence and prevalence of running related injuries are high, creating a barrier to participation. Traditional research paradigms attribute high running injury rates to anatomical factors, training habits, and high peak loads resulting from gait mechanics. However, the specific mechanisms of tibial stress fracture injuries, a serious running-related injury, and why females are at such high risk for these injuries, are largely unknown. Runners often train at variable running speeds and durations that can affect the accumulation of potentially injurious loads, but until recently, studies on running injuries have mostly considered training habits and mechanical loads separately. Therefore, the purpose of this dissertation was to identify how training factors of running speed, volume, and duration contribute to the loads accumulated by the body in relation to tibial stress fracture injury risk. Specifically, this dissertation consists of three studies which determine i) the cumulative load of two proportions of running speed over a constant distance and average pace of running, ii) how fatigue-related gait adjustments affect the loads accumulated per-kilometer within a single prolonged run, and if there is a relationship between gait adjustments and physiological or cognitive fatigue outcomes; and iii) if fatigue-related changes in running gait affect the model-predicted cumulative damage and probability of tibial stress fracture. In study 1, a combination of slow and fast speeds led to greater estimated cumulative load compared to running at all normal speed. The greater cumulative load resulted from greater loading during slow running compared to fast running. In study 2, runners maintained gait mechanics and cumulative loads throughout an easy run to fatigue. In study 3, the model-predicted cumulative damage and probability of tibial stress fracture injury were similar between hypothetically maintained gait and measured fatigue-adjusted gait conditions. These results suggest running volume and average pace are not sufficient metrics for tracking cumulative load, and fatigue during running is not likely a major injury risk factor. Further, these results suggest that other training factors or individual factors may play a greater role in injury development than running speed, volume, or fatigue.