Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

Tracking of Human Joints Using Twist and Exponential Map

Motion tracking system in the home-based environment exhibits attractive advantages for stroke patients. Current methods suffer from incapability of accurately tracking movements with high degree of freedoms. Besides hardly meeting the predefined position during inertial sensor mounting also affects system\u27s performance. To tackle these challenges, a motion tracking system using twist and exponential map technology is developed in this paper. Firstly, a kinematic model for trunk and upper extremity is designed. Based on this model, twist and exponential map method which updates frames in their initial coordinates instead of transforming coordinates from one frame to another presents high efficiency and convenience in estimating joints\u27 position and orientation. In the experiment, multiple movements are tracked by both of inertial sensor system and optical tracking system. Their comparison verifies this system\u27s high accuracy

Line-of-sight-stabilization and tracking control for inertial platforms

Nowadays, line of sight stabilization and tracking using inertially stabilized platforms (ISPs) are still challenging engineering problems. With a growing demand for high-precision applications, more involved control techniques are necessary to achieve better performance. In this work, kinematic and dynamic models for a three degrees-of-freedom ISP are presented. These models are based in the vehicle-manipulator system (VMS) framework for modeling of robot manipulators operating in a mobile base (vehicles). The dynamic model follows the Euler-Lagrange formulation and is implemented by numeric simulations using the iterative Newton-Euler method. Two distinct control strategies for both stabilization and tracking are proposed: (i) computed torque control and (ii) sliding mode control using the recent SuperTwisting Algorithm (STA) combined with a High-Order Sliding Mode Observer (HOSMO). Simulations using data from a simulated vessel allow us to compare the performance of the computed torque controllers with respect to the commonly used P-PI controller. Besides, the results obtained for the sliding mode controllers indicate that the Super-Twisting algorithm offers ideal robustness to the vehicle motion disturbances and also to parametric uncertainties, resulting in a stabilization precision of approximately 0,8 mrad.Hoje em dia, a estabilização e o rastreamento da linha de visada utilizando plataformas inerciais continuam a constituir desafiadores problemas de engenharia. Com a crescente demanda por aplicações de alta precisão, técnicas de controle complexas são necessárias para atingir melhor desempenho. Neste trabalho, modelos cinemáticos e dinâmicos para uma plataforma mecânica de estabilização inercial são apresentados. Tais modelos se baseiam no formalismo para sistemas veículo-manipulator para a modelagem de manipuladores robóticos operando em uma base móvel (veículo). O modelo dinâmico apresentado segue a formulação analítica de Euler-Lagrange e é implementado em simulações numéricas através do método iterativo de Newton-Euler. Duas estratégias de controle distintas para estabilização e rastreamento são propostas: (i) controle por torque-computado e (ii) controle por modos deslizantes utilizando o recente algoritmo Super-Twisting combinado com um observador baseado em modos deslizantes de alta ordem. Simulações utilizando dados de movimentação de um navio simulado permitem comparar o desempenho dos controladores por torque computado em relação a um tipo comum de controlador linear utilizado na literatura: o P-PI. Além disso, os resultados obtidos para o controle por modos deslizantes permitem concluir que o algoritmo Super-Twisting apresenta rejeição ideal a perturbações provenientes do movimento do veículo e também a incertezas paramétricas, resultando em precisão de estabilização de aproximadamente 0,8 mrad

Human pose estimation from video and inertial sensors

[no abstract

Modelling, Optimization and Control of Biomorphic Hands

In this thesis, we present a general framework to model and control a class of biomorphically designed systems for robotic manipulation. Such system, are composed of a set of rigid bodies, interacting through unilateral rolling contact, and are actuated by a net of elastic tendons. Method based on convex analysis are applied to study this class of mechanisms, and are shown to provide a basis for the dynamic control of co-contraction and internal forces that guarantee the correct operation of the system, despite limited friction between contacting surfaces or object fragility. An algorithm is described and tested that integrate a computed torque law, and allows to control tendon actuators to optimally comply with the prescribed constraints

Robot Dynamics and Control Based on Exponential Matrices

In this work was accomplished a review and comparison of the methods which allows make the kinematic and dynamic models. We can distinguish two ways:- Classic and the most common way of representing a multi-link manipulator. In case of kinematic model it is algorithm Denavit-Hartenberg and the homogeneous transformation matrix, as well as the recursive method based on Newton's equations for dynamic model.- An alternative way of representing the multi-link manipulator, which is based on the exponential matrices for the kinematic and dynamic model.I had carried out analysis of manipulator ABB IRB 140. All researching was accomplished on base of this manipulator. Also compiled system description parameters, which required for mathematical model.Calculations were made using two different methods. On the basis of the results compiled two dynamic models describing the manipulator.I had done simulation and comparison the obtained characteristics based on the determined models.In this work was accomplished a review and comparison of the methods which allows make the kinematic and dynamic models. We can distinguish two ways:- Classic and the most common way of representing a multi-link manipulator. In case of kinematic model it is algorithm Denavit-Hartenberg and the homogeneous transformation matrix, as well as the recursive method based on Newton's equations for dynamic model.- An alternative way of representing the multi-link manipulator, which is based on the exponential matrices for the kinematic and dynamic model.I had carried out analysis of manipulator ABB IRB 140. All researching was accomplished on base of this manipulator. Also compiled system description parameters, which required for mathematical model.Calculations were made using two different methods. On the basis of the results compiled two dynamic models describing the manipulator.I had done simulation and comparison the obtained characteristics based on the determined models

A Generative Human-Robot Motion Retargeting Approach Using a Single RGBD Sensor

The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified generative framework without relying on any explicit mapping. First, a 3D parametric human-robot (HUMROB) model is proposed which has the specific joint and stability configurations as the target robot while its shape conforms the source human subject. The robot configurations, including its skeleton proportions, joint limitations, and DoFs are enforced in the HUMROB model and get preserved during the tracking procedure. Using a single RGBD camera to monitor human pose, we use the raw RGB and depth sequence as input. The HUMROB model is deformed to fit the input point cloud, from which the joint angle of the model is calculated and applied to the target robots for retargeting. In this way, instead of fitted individually for each joint, we will get the joint angle of the robot fitted globally so that the surface of the deformed model is as consistent as possible to the input point cloud. In the end, no explicit or pre-defined joint mapping strategies are needed. To demonstrate its effectiveness for human-robot motion retargeting, the approach is tested under both simulations and on real robots which have a quite different skeleton configurations and joint degree of freedoms (DoFs) as compared with the source human subjects