Applying Reinforcement Learning in Treatment Strategies for Cardiogenic Shock Patients

Objectius de Desenvolupament Sostenible::3 - Salut i Benesta

Optimal control prediction of dynamically consistent walking motions

The main objective of this bachelor thesis in Industrial Technology Engineering is to predict dynamically consistent walking motions from kinematic and dynamic measurements obtained at the UPC Biomechanics Laboratory. A healthy gait cycle is captured and foot-ground contact forces are measured. Then, in order to acquire the new motions, optimal control techniques are applied. The human body is modeled with a multibody system formed by rigid bodies. Concretely, a twodimensional simpli ed skeletal model focused on the lower extremity is used in this work. It is formed by a total of 12 rigid bodies (trunk, pelvis and leg segments) and it has 10 degrees of freedom. The inverse dynamic analysis is performed using OpenSim, a free software tool developed by Stanford University that is widely used by the scienti c community. The optimal control algorithm to obtain dynamically consistent walking motions from experimental data is implemented in MATLAB. Moreover, the software used to solve the optimal control problem is GPOPS-II, a general-purpose MATLAB-based software for solving multiple-phase optimal control problems, developed by the University of Florida. Parameters of GPOPS-II are changed to study the in uence on the solution. Then, di erent formulations are analyzed to assess convergence and similarity between the new motion and the captured one. During this report, all the processes involved in the analysis and the related theory are detailed, as well as the methodology used. Theoretical background is presented and complemented with examples of other works. The skeletal model used is described in detail. The process to export and obtain the experimental kinematics and dynamics using OpenSim is explained step by step. Optimal control theory and GPOPS-II working environment, which are employed as the tool to predict new motions, are also explained. And nally, results are presented and discussed. This project is considered an initial study of optimal control techniques to predict human motion. Thereby, it allows to understand these techniques and gain knowledge about how they can be used in order to be applied, in the future, in more complex models

Simulación predictiva de la marcha asistida para la personalización de ortesis activas para lesionados medulares

Postprint (published version

Evaluation of optimal control formulations for obtaining dynamically consistent walking motions

n recent years, interest has grown in predicting human motion, for example, to study cause-effect relations for a specific task [1]. To predict human motion, researchers typically use optimization-based methods that minimize a certain cost function. The objective of this study is to analyze two different optimal control formulations that track experimental data from a healthy gait cycle to obtain a dynamically consistent walking motion, i.e., with minimal residual wrench applied to the pelvis.Postprint (published version

Prediction of three-dimensional crutch walking patterns using a torque-driven model

Computational prediction of 3D crutch-assisted walking patterns is a challenging problem that could be applied to study different biomechanical aspects of crutch walking in virtual subjects, to assist physiotherapists to choose the optimal crutch walking pattern for a specific subject, and to help in the design and control of exoskeletons, when crutches are needed for balance. The aim of this work is to generate a method to predict three-dimensional crutch-assisted walking motions following different patterns without tracking any experimental data. To reach this goal, we collected gait data from a healthy subject performing a four-point non-alternating crutch walking pattern, and developed a 3D torque-driven full-body model of the subject including the crutches and foot- and crutch-ground contact models. First, we developed a predictive (i.e., no tracking of experimental data) optimal control problem formulation to predict crutch walking cycles following the same pattern as the experimental data collected, using different cost functions. To reduce errors with respect to reference data, a cost function combining minimization terms of angular momentum, mechanical power, joint jerk and torque change was chosen. Then, the problem formulation was adapted to handle different foot- and crutch-ground conditions to make it capable of predicting three new crutch walking patterns, one of them at different speeds. A key aspect of our algorithm is that having ground reactions as additional controls allows one to define phases inside the cycle without the need of formulating a multiple-phase problem, thus facilitating the definition of different crutch walking patterns.Postprint (author's final draft

Applying Reinforcement Learning in Treatment Strategies for Cardiogenic Shock Patients

Objectius de Desenvolupament Sostenible::3 - Salut i Benesta

Calibration of foot-ground and crutch-ground contact models for optimal control prediction of crutch-assisted walking motions

Postprint (published version

Personalised active orthoses for SCI subjects based on optimal control prediction

The authors are working on the personalisation of an innovative low-cost, lightweight, and easy-to-use active orthosis to facilitate over-ground walking with crutches by individuals with spinal cord injury (SCI) who possess remaining hip function. Personalisation will involve selection of the best knee motor control strategy for each subject, using predictive walking simulations that combine OpenSim patient-specific models with GPOPS-II optimal control predictions. In the present work, we describe a direct collocation optimal control framework to obtain a dynamically consistent walking motion that reproduces experimental measurements