Browsing by Author "Hettinga, Blayne"
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- ItemOpen AccessGender differences in gait kinematics for patients with knee osteoarthritis(BMC Musculoskeletal Disorders, 2016-04-01) Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne; Kobsar, Dylan; Ferber, ReedBackground: Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts. Methods: One hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data. Results: OA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98–100 % were found between gender groups as well as between OA groups. Conclusions: These results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed.
- ItemOpen AccessKinematic and Kinetic Factors Associated with Start Performance in Elite Luge Athletes(2015-11-19) Tomaghelli, Luciano Sebastian; Katz, Larry; Hettinga, BlayneIn the sport of luge, the start phase plays a critical role in overall race performance. However, the biomechanical factors influencing start performance are currently unexplored. The goal of this thesis was to achieve a better understanding of the biomechanics behind the luge start and to validate the use of an accelerometer to assess continuous sled velocity. It was found that an accelerometer is not a valid tool to evaluate luge sled velocity during the starts. A systematic bias was found in the accelerometer underestimating sled velocity in almost 90% of the trials. For the pull phase of the start, kinetic variables had a high relationship with pull performance. Gender differences were found in the velocity development and relative force application. None of the hypothesis discussed for the paddling phase were related to performance. However, two very distinct mechanisms in the development of sled velocity were found.
- ItemOpen AccessTranslating technology to clinical practice: Predicting how knee osteoarthritis patients will respond to an exercise intervention(2017) Kobsar, Dylan; Ferber, Reed; Hettinga, Blayne; Boyd, JeffreyMuscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the overall goal of this thesis was to identify responders to exercise with a conventional motion capture system and translate these findings into a clinically accessible wearable sensor system. It was found that a conventional motion capture system, in combination with patient-reported outcome measures (e.g., function) collected at the baseline of an exercise intervention can successfully predict responders to treatment with greater than 85% accuracy (chapter three). To translate these findings to the clinical setting, more accessible wearable sensors (e.g., accelerometers) were examined in the remaining chapters. Chapter four found that while a single sensor at the lower back could subgroup some gait patterns, it was not sensitive enough to separate other, more similar, gait patterns. Therefore, the reliability of using multiple wearable sensors was examined in chapter five. The lower back, thigh, shank, and foot were all found to be reliable sensor locations for gait analysis and therefore suitable in the final study as potential predictors of response. Finally, chapter six found that a unique combination of wearable sensor data and patient reported outcome measures could successfully identify responders to an exercise intervention with similar accuracy to the conventional motion capture system. Further, the best limited set of sensors included only the back and thigh. Therefore, these findings suggest the potential development of a simplified two sensor system that can provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.