Assessment of joint kinetics in elite sprint cyclists

Sprint cycling requires the production of explosive muscle power outputs up to very high pedalling rates. The ability to assess muscular function through the course of the sprint would aid training practices for high-level performers. Inverse dynamics provides a non-invasive means of estimating the net muscle actions acting across any joint contributing to movement. However, analysis of joint kinetics requires motion-capture techniques that present some unique challenges for cycling. This thesis presents three studies investigating the application of a custom-designed force pedal system to examine the joint kinetics of elite trained track sprint cyclists. To provide the basis for selecting appropriate testing procedures, study one evaluated differences between two- and three- dimensional techniques while assessing joint kinetics of seated and standing sprint cycling at optimal cadence (the cadence where peak power is delivered). Study two examined the impact of cadence and seating position on joint kinetics, while determining testing reliability using the three-dimensional process. Coefficients of variation were established for between- and within- days repetitions of sprint performance at optimal cadence, and cadences 30% lower and 30% higher, in both seated and standing positions. Study three compared joint kinetics of sprint cycling performance with commonly-applied resistance-training exercises in an elite cycling cohort, in order to better understand training specificity. Joint-specific torque-angular velocity relationships were established from seated and standing sprinting at three cadences and the clean exercise at three loads, with other strength-based exercises examined at maximal load only. Study one determined that flattened projections of the 3D motion into 2D resulted in significant differences in joint powers calculated in the sagittal-plane. When using 2D methods, knee joint power was significantly lower and hip transfer power significantly greater, while hip range of motion was lower and the angle where hip peak power occurred later in the crank cycle. These results indicate that 3D processes should be used where evaluation of absolute values are important, although 2D processes may still be acceptable where relative differences are being assessed. It was observed in Study two that, while crank and total muscle power upheld a quadratic power-cadence relationship, joint-specific powers were uniquely related to cadence and riding position. Crank and joint-specific optimal cadences for power production were distinctly different. The hip displayed a linear maximum power-cadence relationship in seated but quadratic in standing position, with the reverse observed at the knee. Ankle and hip transfer powers both linearly declined with cadence irrespective riding position. In such a case, joint-specific power contribution, hence distribution of muscular effort, cannot be directly inferred from power assessed at the crank. Reliability was highest for crank and total muscle power, particularly at the riders’ optimal cadence. Reliability of joint powers were somewhat lower and uniquely dependent on joint, joint action and trial condition. Results indicate that external power output at the crank is relatively stable across sprints, despite variation in the underlying muscular contributions. Results of study three showed equivalence in the torque-angular velocity relationships at the hip in sprint cycling and different phases of the clean. No such relationship was evident at the knee or ankle. In contrast to the negative linear relationships observed in all other conditions, ankle mechanics in sprinting showed a positive linear relationship highlighting a distinct functional role of this joint. Highest maximal torques at the hip and knee were observed during unilateral single rack pull and step-up exercises, respectively, supporting their efficacy for improving the maximum strength characteristics at these joints. The results of this thesis indicate that joint kinetics are an effective means of assessing muscular performance in highly-trained track sprint cyclists and provide information on the underlying strategies that could not be assessed through conventional testing of power at the crank. The use of 3D processes is recommended where accuracy of assessment and absolute values are important. Flexibility of 2D processes may be advantageous in field-based settings and may be acceptable where only relative change is of interest. High reliability of 3D testing supports its use in monitoring of athletes, with the reliability data presented in this thesis providing an indication of the smallest meaningful changes in various trial conditions. Low coefficients of variation observed in crank and muscle power terms, despite greater variation in joint powers, suggest motor control strategies dynamically respond to task conditions while maintaining a consistent external power. Resistance exercises are seen to display jointspecific profiles that characterise relative hip- or knee- dominance. The comparison of these profiles with those of sprint cycling can help inform exercise selection for strength development of elite riders. The ability to monitor changes and target training intervention at joint level provides a unique approach to athlete development. Outcomes of this thesis support the practical application of joint kinetic assessment in aiding training practices to the highest levels of competition in track sprint cycling. Indeed, the equipment, methods and knowledge obtained from this research is currently applied in the preparation of Australia’s best sprint cyclists

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 402)

This bibliography lists 244 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Nov. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

The biomechanics of human locomotion

Includes bibliographical references. The thesis on CD-ROM includes Animate, GaitBib, GaitBook and GaitLab, four quick time movies which focus on the functional understanding of human gait. The CD-ROM is available at the Health Sciences Library

Advancing clinical evaluation and diagnostics with artificial intelligence technologies

Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

Putting artificial intelligence into wearable human-machine interfaces – towards a generic, self-improving controller

The standard approach to creating a machine learning based controller is to provide users with a number of gestures that they need to make; record multiple instances of each gesture using specific sensors; extract the relevant sensor data and pass it through a supervised learning algorithm until the algorithm can successfully identify the gestures; map each gesture to a control signal that performs a desired outcome. This approach is both inflexible and time consuming. The primary contribution of this research was to investigate a new approach to putting artificial intelligence into wearable human-machine interfaces by creating a Generic, Self-Improving Controller. It was shown to learn two user-defined static gestures with an accuracy of 100% in less than 10 samples per gesture; three in less than 20 samples per gesture; and four in less than 35 samples per gesture. Pre-defined dynamic gestures were more difficult to learn. It learnt two with an accuracy of 90% in less than 6,000 samples per gesture; and four with an accuracy of 70% after 50,000 samples per gesture. The research has resulted in a number of additional contributions: • The creation of a source-independent hardware data capture, processing, fusion and storage tool for standardising the capture and storage of historical copies of data captured from multiple different sensors. • An improved Attitude and Heading Reference System (AHRS) algorithm for calculating orientation quaternions that is five orders of magnitude more precise. • The reformulation of the regularised TD learning algorithm; the reformulation of the TD learning algorithm applied the artificial neural network back-propagation algorithm; and the combination of the reformulations into a new, regularised TD learning algorithm applied to the artificial neural network back-propagation algorithm. • The creation of a Generic, Self-Improving Predictor that can use different learning algorithms and a Flexible Artificial Neural Network.Open Acces

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018