Christian Fleischer

A Human--Exoskeleton Interface Utilizing Electromyography

This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.

Predicting the intended motion with EMG signals for an exoskeleton orthosis controller

In this paper, we present a method to calculate the intended motion of joints in the human body by analysing EMG signals. Those signals are emitted by the muscles attached to the adjoining bones during their activation. With the resulting intended motion, a leg orthosis can be controlled in realtime to support disabled people while walking or climbing stairs and help patients suffering from the effects of a stroke in their rehabilitation efforts. To allow a variety of different motions, a human body model with physical properties is developed and synchronized with data recorded from the pose sensors. Computing the intended motion is performed by converting calibrated EMG signals to muscle forces which animate the model. The algorithm was evaluated with experiments showing the calculated intended motion while climbing one step of a stair. The algorithm and the experimental results are both shown.

Application of EMG signals for controlling exoskeleton robots.

Abstract Exoskeleton robots are mechanical constructions attached to human body parts, containing actuators for influencing human motion. One important application area for exoskeletons is human motion support, for example, for disabled people, including rehabilitation training, and for force enhancement in healthy subjects. This paper surveys two exoskeleton systems developed in our laboratory. The first system is a lower-extremity exoskeleton with one actuated degree of freedom in the knee joint. This system was designed for motion support in disabled people. The second system is an exoskeleton for a human hand with 16 actuated joints, four for each finger. This hand exoskeleton will be used in rehabilitation training after hand surgeries. The application of EMG signals for motion control is presented. An overview of the design and control methods, and first experimental results for the leg exoskeleton are reported.

Calibration of an EMG-Based Body Model with six Muscles to control a Leg Exoskeleton

This paper presents a body model of intermediate level of detail to allow prediction of the knee torque produced by thigh muscles based on EMG signals. This torque prediction is used as input for a torque controller that adapts the level of support offered to an operator by a powered leg orthosis. The level of detail of the body model is chosen in such a way, that all parameters of the model can be calibrated for a specific operator with only a few sensors that are mounted on the exoskeleton.

Embedded Control System for a Powered Leg Exoskeleton

Fig. 1. The exoskeleton for knee support. trol system for a powered orthosis is presented. The orthosis (as shown in Fig. 1) is used to support the thigh muscles during flexion and extension of the knee while performing common motions like getting up from a chair, walking, or climbing stairs. The user interface of the control system is implemented by evaluating EMG signals from significant thigh muscles to find out the intended motion of the subject. The intended motion is executed with a linear actuator to support the subject’s own muscle force. Results from two experiments are presented. During the trials the torque support from the actuator illustrates the performance of the system.

Online calibration of the EMG to force relationship

In this paper we present a method to calibrate the surface EMG signal-to-force-relationship online. For this, a simple biomechanical model composed of bones and muscles is used. The calibration is based on an online optimization algorithm where the error between the movement of the human and the movement computed with the biomechanical model is minimized. The proposed method is part of a control system for an exoskeleton robot that should aid the wearer in everyday-life situations like walking, standing up and sitting down. In contrast to existing methods for the calculation of the EMG signal-to-force-relationship, we are not interested in the exact force values of every single muscle, but our model groups some muscles together and uses the EMG signal of one of those muscles as a representative for the group to simplify calculations. The performance of the presented method was investigated on the leg movement in sagittal plane without contact to the environment.

EMG-Driven Human Model for Orthosis Control

In this paper we present a simplified body model of the human lower extremities used for the computation of the intended motion of a subject wearing an exoskeleton orthosis. The intended motion is calculated by analyzing EMG signals emitted by selected muscles. With the calculated intended motion a leg orthosis is controlled in real-time performing the desired motion.

Research on Exoskeletons at the TU Berlin

We present an overview of the work devoted to exoskeletons which has been performed for the last seven years at TU Berlin. Three different types of exoskeleton devices have been developed: hand and finger exoskeletons for rehabilitation, a leg exoskeleton for motion support, and an arm exoskeleton for multimodal human-computer interaction. The research has been focused on different types of control strategies and algorithms as well as on the implementation of applications. The main part of this work is devoted to using electrical muscle signals for the control systems. We present the concepts for design and application, review the main algorithms and show experimental results and first experience with applications.

Motion Calculation for Human Lower Extremities Based on EMG-Signal-Processing and Simple Biomechanical Model

In this paper we present a way to calculate the motion of the human lower extremities online based on surface EMG signals emitted by the activated muscles. The EMG signal evaluation is directly integrated into the control loop of the model to allow for more flexible and spontaneous movements than with predefined trajectories. The algorithm implements a simple biomechanical model composed of bones and muscles. An additional stability controller modifies the torques in the ankle, knee and hip joints to keep the model in a stable pose. The proposed method will be part of a control system for an exoskeleton robot where the movement of the model will be interpreted as the intended movement of the operator. The performance of the presented method was investigated on the stand-to-sit movement.