Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
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Patients with knee osteoarthritis (OA) often tell us that they put extra load on the joints of the opposite leg as they walk. Multiple joint OA is common and has previously been related to gait changes due to hip OA (Shakoor et al 2002). The aim of this study was to determine whether patients with medial compartment knee OA have abnormal biomechanics of the unaffected knee and both hips during normal level gait. Twenty patients (11 male, 9 female), with severe medial compartment knee OA and no other joint pain were recruited. The control group comprised 20 adults without musculoskeletal pain. Patients were reviewed, x-rays were examined and WOMAC and Oxford knee scores were completed. A 12 camera Vicon (Vicon, Oxford) system was used to collect kinematic data (100Hz) on level walking and the ground reaction force was recorded using three AMTI force plates (1000Hz). Surface electrodes were placed over medial and lateral quadriceps and hamstrings bilaterally to record EMG data (1000Hz). Kinematics and kinetics were calculated using the Vicon ‘plug-in-gait’ model. A co-contraction index was calculated for the EMG signals on each side of the knee, representing the magnitude of the combined readings relative to their maximum contraction during the gait cycle. Statistical comparisons were performed using t-tests with Bonferroni's correction for two variables and ANOVA for more than two variables (SPSS v16).Introduction
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