THE EVOLUTION OF POSE ESTIMATION ALGORITHMS FOR 3D MOTION CAPTURE DATA: COPING WITH UNCERTAINTY

At the heart of many biomechanical analyses is the estimation of the pose (position and orientation) of a multi-segment model based on recording of 3D motion data. The principle assumption of most pose estimation algorithms is that sensors move rigidly with the body segments to which they are attached. It is accepted, however r, that sensors attached to the skin move e relative to the underlying skeleton and that this idiosyncratic Soft Tissue Artifact (STA A) is challenging to model. Usually pose is estimated with discriminative algorithms that are ill-suited to the uncertainty of STA. Emerging algorithms based on probabilistic inference may mitigate STA by encoding the pose e and any prior knowledge about the pose e probabilistically, and capture the “artifacts” using a generative model

AN INVERSE METHOD FOR PREDICTING THE MECHANICS OF HOPPING FROM MOTION DATA INPUT

By segmentation of the body, this study estimated both the natural frequency and mode shapes of the mechanics of hopping, during a stance phase, using a purposely developed three degree-of-freedom state space model of the leg. The model, which was validated via comparison of measured and estimated motion data, incorporated a novel use of the Bellman-Quasilinearization technique estimators. Vertical displacements of the centre of mass of each segment (thigh, shank, and foot) were collected during a stance phase and used as observed data for unknown leg compliance parameters. It was found that the relative joint contributions to compliance during an exhaustive hopping appear to be tuned in part, to the type of foot-surface landing (input signals)

Optimal Reconstruction of Human Motion From Scarce Multimodal Data

Wearable sensing has emerged as a promising solution for enabling unobtrusive and ergonomic measurements of the human motion. However, the reconstruction performance of these devices strongly depends on the quality and the number of sensors, which are typically limited by wearability and economic constraints. A promising approach to minimize the number of sensors is to exploit dimensionality reduction approaches that fuse prior information with insufficient sensing signals, through minimum variance estimation. These methods were successfully used for static hand pose reconstruction, but their translation to motion reconstruction has not been attempted yet. In this work, we propose the usage of functional principal component analysis to decompose multimodal, time-varying motion profiles in terms of linear combinations of basis functions. Functional decomposition enables the estimation of the a priori covariance matrix, and hence the fusion of scarce and noisy measured data with a priori information. We also consider the problem of identifying which elemental variables to measure as the most informative for a given class of tasks. We applied our method to two different datasets of upper limb motion D1 (joint trajectories) and D2 (joint trajectories + EMG data) considering an optimal set of measures (four joints for D1 out of seven, three joints, and eight EMGs for D2 out of seven and twelve, respectively). We found that our approach enables the reconstruction of upper limb motion with a median error of $0.013 \pm 0.006$ rad for D1 (relative median error 0.9%), and $0.038 \pm 0.023$ rad and $0.003 \pm 0.002$ mV for D2 (relative median error 2.9% and 5.1%, respectively)

Feasibility of Using an Equilibrium Point Strategy to Control Reaching Movements of Paralyzed Arms with Functional Electrical Stimulation

Functional electrical stimulation (FES) is a technology capable of improving the quality of life for those with the loss of limb movement related to spinal cord injuries. Individuals with high-level tetraplegia, in particular, have lost all movement capabilities below the neck. FES has shown promise in bypassing spinal cord damage by sending electrical impulses directly to a nerve or muscle to trigger a desired function. Despite advancements in FES, full-arm reaching motions have not been achieved, leaving patients unable to perform fundamental tasks such as eating and grooming. To overcome the inability in current FES models to achieve multi-joint coordination, a controller utilizing muscle activations to achieve full-arm reaching motions using equilibrium point control on a computer-simulated human arm was developed. Initial simulations performed on the virtual arm generated muscle activations and joint torques required to hold a static position. This data was used as a model for Gaussian Process Regression to obtain muscle activations required to hold any desired static position. The accuracy of the controller was tested on twenty joint positions and was capable of holding the virtual arm within a mean of 1.1 ± 0.13 cm from an original target position. Once held in a static position, external forces were introduced to the simulation to evaluate if muscle activations returned the arm towards the original position after being moved away within a basin of attraction. It was found that the basin of attraction was limited to a 15 cm sphere around the joint position, regardless of position in the workspace. Muscle activations were then tested and found to successfully perform movements between points within the basin. The development of a controller capable of equilibrium point controlled movement is the initial step in recreating these movements in high-level tetraplegia patients with an implanted FES

Feasibility of Using an Equilibrium Point Strategy to Control Reaching Movements of Paralyzed Arms with Functional Electrical Stimulation

Functional electrical stimulation (FES) is a technology capable of improving the quality of life for those with the loss of limb movement related to spinal cord injuries. Individuals with high-level tetraplegia, in particular, have lost all movement capabilities below the neck. FES has shown promise in bypassing spinal cord damage by sending electrical impulses directly to a nerve or muscle to trigger a desired function. Despite advancements in FES, full-arm reaching motions have not been achieved, leaving patients unable to perform fundamental tasks such as eating and grooming. To overcome the inability in current FES models to achieve multi-joint coordination, a controller utilizing muscle activations to achieve full-arm reaching motions using equilibrium point control on a computer-simulated human arm was developed. Initial simulations performed on the virtual arm generated muscle activations and joint torques required to hold a static position. This data was used as a model for Gaussian Process Regression to obtain muscle activations required to hold any desired static position. The accuracy of the controller was tested on twenty joint positions and was capable of holding the virtual arm within a mean of 1.1 ± 0.13 cm from an original target position. Once held in a static position, external forces were introduced to the simulation to evaluate if muscle activations returned the arm towards the original position after being moved away within a basin of attraction. It was found that the basin of attraction was limited to a 15 cm sphere around the joint position, regardless of position in the workspace. Muscle activations were then tested and found to successfully perform movements between points within the basin. The development of a controller capable of equilibrium point controlled movement is the initial step in recreating these movements in high-level tetraplegia patients with an implanted FES

Étude de l’impact de l’incertitude des processus de reconstruction 3D et de recalage sur l’analyse de la cinématique du genou

Analyser la cinématique du genou d’un patient permet aux médecins d’évaluer sa mobilité. Pour effectuer cette analyse, il est nécessaire d’acquérir les données cinématiques du patient pendant le mouvement et de les traiter. L’évaluation à travers le temps est effectuée en comparant les caractéristiques du mouvement issues du traitement des données entre chaque acquisition. Cependant, les variations de ces caractéristiques peuvent être dues à un changement réel du mouvement effectué pendant l’acquisition ou à des erreurs de mesure ou des erreurs de modélisation. Ainsi, distinguer les sources de ces variations permettrait aux cliniciens de minimiser le risque de mauvaise interprétation. Ce projet de maîtrise s’intéresse aux erreurs de traitement des données issues du processus d’acquisition des données lui-même. S’intéressant aux processus visualisant en temps réel le mouvement anatomique du genou, ce projet porte sur l’acquisition de données cinématiques mais aussi des données morphologiques. Ainsi, des étapes de reconstruction 3D des os et de recalage seront des étapes déterminantes du processus. Ce projet étudie l’impact des incertitudes des étapes de reconstruction 3D et de recalage du processus d’acquisition sur l’imprécision du traitement de ces données acquises. Aussi, ce projet propose une intégration des imprécisions du traitement des données dans leur visualisation en temps-réel. Pour cela, un simulateur du mouvement de l’articulation du genou développé par l’entreprise partenaire, a permis d’obtenir des données cinématiques de ce mouvement. Ces données ont été traitées par une décomposition du mouvement selon ses six degrés de liberté. L’incertitude du processus d’acquisition des données cinématiques est simulée par la méthode de Monte Carlo. Vingt-quatre paramètres déterminant l’incertitude des étapes de reconstruction 3D et de recalage du processus ont été étudiés. Le lien entre l’incertitude du processus d’acquisition et la quantification de la marche est établi grâce à une équation mathématique obtenue par validation croisée et régression pas à pas avant. Une interface utilisateur a été implémentée pour illustrer un exemple de visualisation des résultats de caractérisation prenant en compte ces imprécisions. Les résultats de la méthode de Monte Carlo et de la modélisation indiquent que les erreurs d’analyse de la cinématique par la décomposition en six degrés de liberté du mouvement sont fortement corrélées aux valeurs des incertitudes de l’étape de recalage (le coefficient de corrélation maximum est de 0.85). Les incertitudes de la reconstruction sont moins corrélées que celles du recalage aux valeurs des erreurs. Ainsi, cette étude permet de mettre en évidence l’importance de la résolution de l’étape de recalage. Également, elle appuie les bonnes pratiques déjà entamées en reconstruction 3D par l’ensemble de la communauté scientifique. En effet, la minimisation de l’influence des incertitudes de la reconstruction est due à l’utilisation de points issus de formes géométriques recalées sur des régions des reconstructions 3D, par opposition à des points acquis individuellement. Il serait donc pertinent d’accentuer les études de réduction de l’incertitude de l’étape de recalage.----------ABSTRACT Knee kinematic analysis is used to evaluate the mobility of patients. This evaluation is performed by comparing movement characteristics between each movement acquisition. However, variations of those characteristics may be due to a change in motion during acquisition, to measurement errors or to modeling errors. Thus, distinguishing the sources of these variations would allow clinicians to minimize the risk of misinterpretation. This master project aims to address the data errors from the data acquisition process itself. Focusing on real-time knee anatomical movement visualization processes, this project is centered on the kinematic and morphological data acquisition. Thus, 3D bone reconstruction and registration steps will be critical steps in the acquisitions. This project studies 3D reconstruction and registration step precision impact on data acquired inaccuracy. Also, this project suggests a data inaccuracies integration in their real-time visualization. A knee joint movement simulator developed by the collaborative company has allows to obtain kinematic data of the knee movement. These data were processed by a movement decomposition into six degrees of freedom. Kinematic data acquisition inaccuracy is simulated by a Monte Carlo method. Twenty-four parameters determining 3D reconstruction and registration steps inaccuracy were studied. A link between acquisition process inaccuracy and knee kinematic quantification is established by mathematical equation from cross-validation and step-by-step regression. A user interface has been implemented to illustrate characterization results with their inaccuracies. Monte Carlo method and modeling indicate that knee kinematic analysis errors by the decomposition in six degrees of freedom of movement are strongly correlated with registration step inaccuracy values (correlation coefficients maximum is 0.85). The 3D reconstruction step inaccuracies are less correlated to error values. This study highlights the importance of registration step resolution. In addition, it validates current practices in 3D reconstruction used by the whole scientific community. Indeed, 3D reconstruction impact on knee kinematic analysis is minimized by the use of points resulting from geometrical shapes registred on 3D reconstruction parts, opposed to points acquired individually. Therefore, it would be relevant to carry out further studies on inaccuracy reduction of the registration step

Achieving Practical Functional Electrical Stimulation-driven Reaching Motions In An Individual With Tetraplegia

Functional electrical stimulation (FES) is a promising technique for restoring the ability to complete reaching motions to individuals with tetraplegia due to a spinal cord injury (SCI). FES has proven to be a successful technique for controlling many functional tasks such as grasping, standing, and even limited walking. However, translating these successes to reaching motions has proven difficult due to the complexity of the arm and the goaldirected nature of reaching motions. The state-of-the-art systems either use robots to assist the FES-driven reaching motions or control the arm of healthy subjects to complete planar motions. These controllers do not directly translate to controlling the full-arm of an individual with tetraplegia because the muscle capabilities of individuals with spinal cord injuries are unique and often limited due to muscle atrophy and the loss of function caused by lower motor neuron damage. This dissertation aims to develop a full-arm FES-driven reaching controller that is capable of achieving 3D reaching motions in an individual with a spinal cord injury. Aim 1 was to develop a complete-arm FES-driven reaching controller that can hold static hand positions for an individual with high tetraplegia due to SCI. We developed a combined feedforward-feedback controller which used the subject-specific model to automatically determine the muscle stimulation commands necessary to hold a desired static hand position. Aim 2 was to develop a subject-specific model-based control strategy to use FES to drive the arm of an individual with high tetraplegia due to SCI along a desired path in the subject’s workspace. We used trajectory optimization to find feasible trajectories which explicitly account for the unique muscle characteristics and the simulated arm dynamics of our subject with tetraplegia. We then developed a model predictive control controller to iii control the arm along the desired trajectory. The controller developed in this dissertation is a significant step towards restoring full arm reaching function to individuals with spinal cord injuries