<p> Understanding human movement requires that biomechanists have knowledge of the kinematics and kinetics of the motion. Calculating the internal kinetics of a movement requires the input of segment inertial characteristics. Errors in the estimations of these body segment parameters (BSPs) may have detrimental effects on segmental kinetic calculations. </p> <p> The purposes of this study were to use i) investigate a new technique for measuring BSPs using dual photon absorptiometry (DPX) and ii) to investigate population differences in BSP values, develop geometric models to predict BSPs and compare geometric predictions with other prediction methods. </p> <p> In study 1, DPX measured whole body mass of humans with a group mean percent difference of -1.05% from criterion measurements. DPX also measured mass, centre of mass along a transverse axis (CM) and moment of inertia about the centre of mass (ICG) of a homogeneous object and a human cadaver leg with percent errors less than 4% from criterion measurements. </p> <p> In Study 2, 1 00 subjects were selected from four subpopulation groups according to gender (males/females) and age (19-30/ 55+ years). Using DPX, six body segments were measured for mass (forearm, hand, thigh, leg, foot, head) and four were measured for CM and radius of gyration (forearm, thigh, leg, head). Linear regression equations were developed and compared with geometric predictions and prediction equations from a popular literature source (Winter, 1990). </p> <p> Population differences were statistically significant for all body segments and all segment parameters except hand mass. Large segmental differences between individuals of similar size were also observed. The results showed the linear regression equations to provide the best estimations of BSPs. The geometric models and the predictions from Winter (1990) were poor for most segments. </p> <p> This study provided the foundation for a new method of BSP prediction. The population specific linear regression equations developed in this study should be used to predict BSPs for individuals similar to those examined in this experiment. While geometric models provided poor predictions, future improvements may increase their performance. </p> / Thesis / Candidate in Philosophy

Differences in gait biomechanics have been observed between obese and healthy weight adults. It is possible that body segment parameters, particularly the thigh, contribute to the differences in knee biomechanics observed during gait between obese and healthy weight adults. The purpose of this study was to determine if increases in thigh circumference and/or mass associated with obesity alter walking biomechanics in healthy weight males and females. Thigh mass and circumference were increased proportional to a 10 unit increase in body mass index. Frontal and sagittal plane knee angles and moments, and temporospatial variables were recorded. For all dependent variables no main effect for gender was observed. Peak knee flexion angle was similar across conditions with no interaction. There was an interaction for peak internal knee extension moment however post hoc comparisons did not reveal differences in condition among males or females. A main effect for condition was observed for peak knee adduction angle, however post hoc comparisons did not reveal differences among conditions. Peak internal knee abduction moment was similar across conditions with no interaction. Stance time and step width increased during the experimental conditions compared to the control. A interaction was observed for stance time. Females had a longer stance time during the circumference only condition compared to the control condition. A greater step width was observed in conditions that increased thigh circumference. Overall, thigh segment parameters altered gait temporospatial variables. Increases in stance time and step width in obese adults compared to healthy weight adults could be a result of their larger thigh segment parameters.

<p> An accurate estimation of Body Segment Parameters (BSPs) is needed to understand human movement. These include segment mass, centre of mass, and moment of inertia about the centre of mass. Bone density scanners, such as DPX, can measure BSPs, but are limited to only two dimensions. MRI produces images in three dimensions, but cannot directly measure mass. For this study, MRI was used in conjunction with a DPX scan of the human body. The result was the development of a method to estimate mass, and subsequently, centre of mass, and moment of inertia from MRI images. Next, ellipses were created from the dimensions of transverse plane slices (produced from MRI). Three different density profiles were applied to the ellipses, and mass, centre of mass and moment of inertia about the centre of mass of each slice was calculated. It was found that constant density transverse plane ellipses could be used to estimate BSPs for most regions of the body. Photogrammetry can also be used to generate the dimensions of ellipses that represent transverse plane slices. Therefore, the suitability of photogrammetry to estimate slice BSPs was tested. It was found that depending on the density profile used, photogrammetry is an effective method for estimating BSPs. An exception to this estimation was in the chest, where ellipses may not be representative of the body. </p> / Thesis / Master of Science (MSc)

L'objectif de ce travail est d'identifier les paramètres inertiels segmentaires humains, c’est-à-dire la masse, la position du centre de masse et la matrice d’inertie de ces segments. Ces paramètres, au nombre de dix par segment, constituent une donnée d'entrée indispensable aux calculs de dynamique inverse utilisés dans les études de biomécanique. Bien qu'il existe des méthodes pour avoir accès à ces paramètres par le biais de tables anthropométriques ou le calcul des volumes segmentaires, l'identification apparaît nécessaire dès lors que les sujets étudiés sont atypiques (handicapés physiques, femmes enceintes, sportifs présentant des hypertrophies musculaires). L'originalité de ce travail est de proposer une approche mixte dans l'écriture du problème d'identification combinant une formulation vectorielle et matricielle des équations du mouvement d’un système poly-articulé supposé rigide, qui ont déjà été établies au sein de l'équipe dans les travaux de thèse de Tony Monnet. La première permet d’identifier les masses et les centres de masse segmentaires. La deuxième permet d’identifier, elle, les matrices d’inertie segmentaires. Les paramètres d’entrée de cette méthode d’identification sont les matrices rotations segmentaires, leurs dérivées secondes, les accélérations segmentaires, ainsi que le torseur externe. Si ce dernier est directement mesuré par une plateforme de force, les autres grandeurs sont obtenues après des opérations sur la mesure de la cinématique segmentaire du sujet obtenue par un système opto-électronique. Ce système mesurant la cinématique du sujet grâce à des marqueurs cutanés, cette cinématique diffère de la cinématique théorique obtenue si les segments sont rigides, du fait des mouvements des masses molles. Ce travail a donc porté sur le calcul d’une matrice rotation optimale, basée sur une transformation matérielle décrivant le mouvement segmentaire.De plus les mouvements des masses molles ainsi que les instruments de mesure induisent un bruit dans les signaux cinématiques. Du fait de la double dérivation de ces signaux pour le calcul des accélérations segmentaires et des dérivées secondes des matrices rotations, ce bruit devient prépondérant sur le signal porteur. Ce travail a donc également porté sur le filtrage à effectuer pour atténuer ces bruits. Cinq filtres utilisés dans la littérature (filtre de Butterworth, lissage de Savitsky-Golay, moyenne glissante, lissage par spline et analyse spectrale) ont été implémentés et leurs effets sur les paramètres inertiels identifiés ont été comparés. Les résultats montrent que les paramètres identifiés avec la méthode vectorielle ne nécessitent pas de traitement. L’identification des matrices d’inertie nécessite, elle, un traitement et le lissage optimal est obtenu avec le moyenne glissante.Enfin, une modélisation du membre supérieur par une chaîne cinématique a également été implémentée afin de rigidifier la cinématique acquise. Les premiers résultats ne sont pas satisfaisants mais le modèle retenu peut être affiné avant de conclure sur l’intérêt de cette modélisation pour l’identification des paramètres inertiels. Finalement, l’approche mixte développée permet l'identification des dix paramètres inertiels des segments du corps humain. La méthode a été validée en identifiant les paramètres inertiels des segments constituant le membre supérieur de dix huit sujets. Les paramètres obtenus ont ensuite été comparés à ceux issus d’une table anthropométrique. Les résultats montrent que les paramètres identifiés sont très proches de ceux estimés. Cela montre donc que l’identification des paramètres inertiels est fiable et permet d’avoir accès aux paramètres inertiels de sujets atypiques, pour qui les tables anthropométriques ne sont pas disponibles. / The aim of this thesis is the identification of body segment inertial parameters (BSIP), i.e. the segment mass, center of mass location and inertia tensor. Those ten parameters per segment are a mandatory input for inverse kinetics methods which are widely used in biomechanics studies. Despite the fact that methods exist to estimate them from anthropometric tables or segment volumes measurements, identification is useful when subjects are atypical (such as disabled people, pregnant women or athletes with muscular hypertrophies). The originality of this work is to use a mixed approach to write the identification problem, combining a vectorial and a matrix formulations of rigid multi-body motion equations, based on previous work did in the RoBioSS axis by Tony Monnet during his PhD. The first one permit to identify segmental masses and center of mass locations. The second one identifies segmental inertia tensors.Inputs of identification algorithm are rotation matrices, their second derivatives, segmental accelerations, and external torsor. Even though this external torsor is directly measured with a force plate, the others inputs are derived from kinematics measurements performed by an optoelectronical device. This device measures kinematics with skin mounted markers tracked by cameras, and the obtained kinematics deviate from the theoretical kinematics of rigid bodies, because of the soft tissues artefacts. In order to deal with these artefacts an optimal rotation matrix computation, based on material transformation, has been performed.Also, noise appears during measurement because of the soft tissues artefacts and the measure device. When double numerical derivatives are applied, this noise becomes greater than the carrier signal. In order to deal with it, five filters, i.e. Butterworth filter, Savitsky-Golay smoothing, sliding average window, spline smoothing and singular spectrum analysis, taken from literature have been implemented and compared. Results show that BSIP identify from vectorial formulation didn’t need any filtering. On the other hand, inertia tensors identification needed smoothed inputs and the best way to smooth them was the sliding average window.Finally, a kinematic chain model of the upper limb has been implemented to rigidify the kinematics. Preliminary results aren’t satisfying but the chain model can be improved before assuming kinematic chain aren’t well suited to enhance BSIP identification. Ultimately, the developed mixed approach has been validated by upper limb inertial parameters identification of eighteen subjects. Identified inertial parameters have also been compared with ones estimated with an anthropometric table. The conclusion is that the identified parameters were very close to the estimated ones, which shows that identification will be reliable to estimate inertial parameters of atypical subjects for whom anthropometric tables aren’t available.