Patients with neurological gait impairments fear to fall while performing locomotor tasks like sit to stand, stand to sit, and level-ground walking without assistance. This prevents them from participating in daily life. Multi-directional Body-Weight Support (BWS) systems aim to assist training in a safe environment to overcome this limitation. To ensure the safety and comfort during training, BWS systems should effectively and automatically assist patients with forces that support the desired locomotor task.Instead of manual switching of controllers through the therapist, we propose a machine learning-based motion onset recognition model that aims at automatic controller switching (4 gait related task classes in this paper). In addition to the data provided by the gait rehabilitation robot FLOAT, data of three Inertial Measurement Units (IMUs) attached to the sternum and middle of the outer thighs on 6 healthy participants were used to predict motion onset of these 4 gait-related tasks.Four train data sets have been built up from the synchronously obtained data from the IMUs and the BWS system by dividing them into observation windows with sizes of 100 ms and 200 ms and overlap factors of 50% and 90%. From each training dataset, 108 time-domain features have been extracted, ranked, and reduced using the Minimum Redundancy Maximum Relevance (MRMR) method from each train set before applying it to the classifier. The dominant features were applied for training and comparing four different classifiers. The performance of the classifiers has been evaluated based on leave one participant out cross-validation. The ensemble classifier obtained from the train data set with a window size of 100 ms and 90% overlap achieved the best performance with an F1_score of 83.7%.

This paper presents a parallel robotic mechanism for endoscope tip stabilization and guidance for a robot-assisted minimally invasive laser osteotome. The mechanism attaches to the bone of the patient, providing a stable and robust platform for the laser integrated in the endoscope tip which has to be moved precisely in the sub-millimeter range along a preoperatively planned path. This method is only possible because cutting bone with laser instead of using conventional bone drills and saws involves considerably lower interaction forces. The design, kinematics, control, and motion performance of the concept are presented for an upscaled prototype. The obtained deviation of the endoscope tip motion from the reference path lies in the sub-millimeter range. This result allows us to conclude that the concept is more than promising. Furthermore, we expect that the herein presented principle will influence the way osteotomies will be performed in the future.

Increasing the transparency of robotic systems used for gait rehabilitation can improve training of various daily activities such as walking or balancing. However, until today, there has been very little effort invested in a systematic analysis of occurring unwanted interaction forces between the patient and the robot. In this paper, we have developed such a setup for systematic analysis and quantification of interaction forces between the gait rehabilitation device the FLOAT and the patient. The results of our analysis show that our setup is capable of quantifying small interaction forces and movement effects under different, precisely controllable experimental conditions. Measured interaction forces of the FLOAT were found to be small and in the range of 3.3 N at low body weight support (52 N) to 17.6 N at high body weight support (376 N) when moving with constant velocity. In addition, deviations of the measured forces in the FLOAT ranged from 2 to 5 N. In combination with additional measurements that investigate other robot workspace locations and movement directions these results will help to understand the influence of unwanted interaction forces on patient recovery.

Nowadays, “user-cooperative” control strategies are commonly used in robot-assisted motor (re-)learning. User-cooperative strategies enable compliant haptic interactions between robot and user: the robot only intervenes as needed, instead of forcing the user to follow a fixed predefined movement. However, the effectiveness of user-cooperative control is contro-versially discussed. Recent studies indicate that the effectiveness of user-cooperative control strategies will be enhanced when every user is individually provided with an optimal amount of assistance or challenge. In conventional motor (re-)learning, such an optimal amount of assistance or challenge is successfully applied by physiotherapists and trainers.

The number of ropes for a fully constrained, tendon-based robot has to be larger than the actuated degrees of freedom since ropes only impose unidirectional constraints. This actuation redundancy implicates that more position information is available than would be required for the the determination of the end-effector pose. This leads to an optimization problem for the forward kinematics of the robot which has to be solved in real-time. Furthermore, the kinematics of tendon-based robots are often kept simple in existing systems by guiding the ropes through holes into the workspace. This facilitates the description of the rope vectors. However, this solution is not applicable for high-load applications, as friction would cause excessive non-linearities and wear. To solve the forward kinematics of tenon-based robots, we introduce a physics-based interpretation of the mentioned optimization problem. The robotic system is described as a damped oscillator whose resting position is equal to the optimal solution. As a major advantage over the known algorithms, this physics-based approach is quantifiable in terms of accuracy of the solution and number of iterations. Furthermore, the design and mathematical description of a deflection unit's geometry is presented. This deflection unit guides the rope smoothly into the workspace and its relevant influence on the kinematic equations can be compensated. The physics-based approach is experimentally evaluated on a tendon-based haptic interface, the r3-system, and it is compared to the solutions using only the minimum set of sensor information.

This paper presents the design of a new user-cooperative rope robot. This robot serves as a large-scale haptic interface in a multi-modal Cave environment used for sport simulation. In contrast to current rope robots, the configuration of the presented robot is adaptable to different simulation tasks what makes the robot more versatile. However, this adaptability and the high dynamics in sports lead to challenging requirements and specific design criteria of the hardware components. We present the requirements on the single robot components as well as the design of the entire setup optimized in terms of user-cooperativity and versatility. The setup includes sensors to measure the relevant parameters for user-cooperative control, i.e. position with a high resolution and the rope forces. Furthermore, an algorithm is introduced, which calculates the distance between the single ropes and the user in order to avoid collisions between the ropes and the user. Single points on the user's body are, therefore, tracked with a motion tracking system; the user's single body parts are then represented by geometrical objects whose distances to the ropes are calculated. The algorithm is programmed in such way that the collision detection runs in real-time. Both, the hardware and the algorithm, were evaluated experimentally in two applications, a rowing simulator and a tennis application. The hardware concept combined with the distance calculation allows the use of new kinematic concepts and expands the spectrum of realizable movement tasks that can be implemented into the Cave environment.