Development of a speed protector to optimize user experience in 3D virtual environments

Virtual walking in virtual environments (VEs) requires locomotion interfaces, especially when the available physical environment is smaller than the virtual space due to virtual reality facilities limitations; many navigation approaches have been proposed according to different input conditions, target selection and speed selection. With current technologies, the virtual locomotion speed for most VR systems relies primarily on rate-control devices (e.g., joystick). The user has to manage manual adaptation of the speed, based on the size of the VE and personal preferences. However, this method cannot provide optimal speeds for locomotion as the user tends to change the speed involuntarily due to non-desired issues including collisions or simulator sickness; in this case, the user may have to adjust the speed frequently and unsmoothly, worsening the situation. Therefore, we designed a motion protector that can be embedded into the locomotion system to provide optimal speed profiles. The optimization process aims at minimizing the total jerk when the user translates from an initial position to a target, which is a common rule of the human motion model. In addition to minimization, we put constraints on speed, acceleration and jerk so that they do not exceed specific thresholds. The speed protector is formulated mathematically and solved analytically in order to provide a smooth navigation experience with a minimum jerk of trajectory. The assessment of the speed protector was conducted in a user study measuring user experience with a simulator sickness questionnaire, event-related skin conductance responses (ER-SCR), and a NASA-TLX questionnaire, showing that the designed speed protector can provide more natural and comfortable user experience with appropriate acceleration and jerk as it avoids abrupt speed profiles.China Scholarship Council: No. 20170839001

Optimisation numérique pour la robotique et exécution de trajectoires référencées capteurs

Le travail présenté dans cette thèse est divisé en deux parties. Dans la première partie, un modèle pour la représentation unifiée de problèmes d'optimisation numérique est proposé. Ce modèle permet de définir un problème d'optimisation indépendamment de la stratégie utilisée pour le résoudre. Cette représentation unifiée est particulièrement appréciable en robotique où une solution analytique des problèmes est rarement possible. La seconde partie traite de l'exécution de mouvements complexes asservis sur un robot humanoïde. Lors de la locomotion d'un tel système, les glissements des points de contact entraînent une dérive qu'il est nécessaire de corriger. Nous proposons ici un modèle permettant d'asservir une tâche de locomotion sur un capteur externe afin de compenser les erreurs d'exécution des mouvements. Un modèle est également proposé permettant de représenter des séquences de tâches de locomotion et de manipulation asservies. Enfin, une méthodologie pour le développement d'applications robotiques complexes est établie. Les stratégies proposées dans le cadre de cette thèse ont été validées sur la plate-forme expérimentale HRP-2. ABSTRACT : The presented work is divided into two parts. In the first one, an unified computer representation for numerical optimization problems is proposed. This model allows to define problems independently from the algorithm used to solve it. This unified model is particularly interesting in robotics where exact solutions are difficult to find. The second part is dealing with complex trajectory execution on humanoid robots with sensor feedback. When a biped robots walks, contact points often slip producing a drift which is necessary to compensate. We propose here a closed-loop control scheme allowing the use of sensor feedback to cancel execution errors. To finish, a method for the the development of complex robotics application is detailed. This thesis contributions have been implemented on the HRP-2 humanoid robot

Optimisation numérique pour la robotique et exécution de trajectoires référencées capteurs

Le travail présenté dans cette thèse est divisé en deux parties. Dans la première partie, un modèle pour la représentation unifiée de problèmes d'optimisation numérique est proposé. Ce modèle permet de définir un problème d'optimisation indépendamment de la stratégie utilisée pour le résoudre. Cette représentation unifiée est particulièrement appréciable en robotique où une solution analytique des problèmes est rarement possible. La seconde partie traite de l'exécution de mouvements complexes asservis sur un robot humanoïde. Lors de la locomotion d'un tel système, les glissements des points de contact entraînent une dérive qu'il est nécessaire de corriger. Nous proposons ici un modèle permettant d'asservir une tâche de locomotion sur un capteur externe afin de compenser les erreurs d'exécution des mouvements. Un modèle est également proposé permettant de représenter des séquences de tâches de locomotion et de manipulation asservies. Enfin, une méthodologie pour le développement d'applications robotiques complexes est établie. Les stratégies proposées dans le cadre de cette thèse ont été validées sur la plate-forme expérimentale HRP-2.The presented work is divided into two parts. In the first one, an unified computer representation for numerical optimization problems is proposed. This model allows to define problems independently from the algorithm used to solve it. This unified model is particularly interesting in robotics where exact solutions are difficult to find. The second part is dealing with complex trajectory execution on humanoid robots with sensor feedback. When a biped robots walks, contact points often slip producing a drift which is necessary to compensate. We propose here a closed-loop control scheme allowing the use of sensor feedback to cancel execution errors. To finish, a method for the the development of complex robotics application is detailed. This thesis contributions have been implemented on the HRP-2 humanoid robot.TOULOUSE-INP (315552154) / SudocSudocFranceF

Temporal Segmentation of Human Motion for Rehabilitation

Current physiotherapy practice relies on visual observation of patient movement for assessment and diagnosis. Automation of motion monitoring has the potential to improve accuracy and reliability, and provide additional diagnostic insight to the clinician, improving treatment quality, and patient progress. To enable automated monitoring, assessment, and diagnosis, the movements of the patient must be temporally segmented from the continuous measurements. Temporal segmentation is the process of identifying the starting and ending locations of movement primitives in a time-series data sequence. Most segmentation algorithms require training data, but a priori knowledge of the patient's movement patterns may not be available, necessitating the use of healthy population data for training. However, healthy population movement data may not generalize well to rehabilitation patients due to large differences in motion characteristics between the two demographics. In this thesis, four key contributions will be elaborated to enable accurate segmentation of patient movement data during rehabilitation. The first key contribution is the creation of a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application specific requirements, algorithm mechanics, and validation techniques. This framework provides a structure for considering the factors that must be incorporated when constructing a segmentation and identification algorithm. The framework enables systematic comparison of different segmentation algorithms, provides the means to examine the impact of each algorithm component, and allows for a systematic approach to determine the best algorithm for a given situation. The second key contribution is the development of an online and accurate motion segmentation algorithm based on a classification framework. The proposed algorithm transforms the segmentation task into a classification problem by modelling the segment edge point directly. Given this formulation, a variety of feature transformation, dimensionality reduction and classifier techniques were investigated on several healthy and patient datasets. With proper normalization, the segmentation algorithm can be trained using healthy participant data and obtain high quality segments on patient data. Inter-participant and inter-primitive variability were assessed on a dataset of 30 healthy participants and 44 rehabilitation participants, demonstrating the generalizability and utility of the proposed approach for rehabilitation settings. The proposed approach achieves a segmentation accuracy of 83-100%. The third key contribution is the investigation of feature set generalizability of the proposed method. Nearly all segmentation techniques developed previously use a single sensor modality. The proposed method was applied to joint angles, electromyogram, motion capture, and force plate data to investigate how the choice of modality impacts segmentation performance. With proper normalization, the proposed method was shown to work with various input sensor types and achieved high accuracy on all sensor modalities examined. The proposed approach achieves a segmentation accuracy of 72-97%. The fourth key contribution is the development of a new feature set based on hypotheses about the optimality of human motion trajectory generation. A common hypothesis in human motor control is that human movement is generated by optimizing with respect to a certain criterion and is task dependent. In this thesis, a method to segment human movement by detecting changes to the optimization criterion being used via inverse trajectory optimization is proposed. The control strategy employed by the motor system is hypothesized to be a weighted sum of basis cost functions, with the basis weights changing with changes to the motion objective(s). Continuous time series data of movement is processed using a sliding fixed width window, estimating the basis weights of each cost function for each window by minimizing the Karush-Kuhn-Tucker optimality conditions. The quality of the cost function recovery is verified by evaluating the residual. The successfully estimated basis weights are averaged together to create a set of time varying basis weights that describe the changing control strategy of the motion and can be used to segment the movement with simple thresholds. The proposed algorithm is first demonstrated on simulation data and then demonstrated on a dataset of human subjects performing a series of exercise tasks. The proposed approach achieves a segmentation accuracy of 74-88%