Dynamic Modeling and Interactive Performance of PARM: A Parallel Upper-Limb Rehabilitation Robot Using Impedance Control for Patients after Stroke.

The robot-assisted therapy has been demonstrated to be effective in the improvements of limb function and even activities of daily living for patients after stroke. This paper presents an interactive upper-limb rehabilitation robot with a parallel mechanism and an isometric screen embedded in the platform to display trajectories. In the dynamic modeling for impedance control, the effects of friction and inertia are reduced by introducing the principle of virtual work and derivative of Jacobian matrix.

Model-Based Estimation of Ankle Joint Stiffness.

We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals.

Observer-Based Human Knee Stiffness Estimation.

Objective: We consider the problem of stiffness estimation for the human knee joint during motion in the sagittal plane.

Methods: The new stiffness estimator uses a nonlinear reduced-order biomechanical model and a body sensor network (BSN). The developed model is based on a two-dimensional knee kinematics approach to calculate the angle-dependent lever arms and the torques of the muscle-tendon-complex.

Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems.

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed "Integrated Posture and Activity Network by Medit Aachen" (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN.

Body-Sensor-Network-Based Spasticity Detection.

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN).

Body sensor network-based strapdown orientation estimation: application to human locomotion.

In this contribution, inertial and magnetic sensors are considered for real-time strapdown orientation tracking of human limb or robotic segment orientation. By using body sensor network integrated triaxial gyrometer, accelerometer, and magnetometer measurements, two orientation estimation filters are presented and subsequently designed for bias insensitive tracking of human gait. Both filters use quaternions for rotation representation, where preprocessing accelerometer and magnetometer data is conducted with the quaternion based estimation algorithm (QUEST) as a reference filter input.

Simultaneous automatic control of oxygen and carbon dioxide blood gases during cardiopulmonary bypass.

In this work an automatic control strategy is presented for the simultaneous control of oxygen and carbon dioxide blood gas partial pressures to be used during cardiopulmonary bypass surgery with heart-lung machine support. As the exchange of blood gases in the artificial extracorporeal lung is a highly nonlinear process comprising varying time delays, uncertainties, and time-varying parameters, it is currently being controlled manually by specially trained perfusionist staff. The new control strategy includes a feedback linearization routine with augmented time-delay compensation and two external linear gain-scheduled controllers, for partial oxygen and carbon dioxide pressures.