Fabian Just

Online adaptive compensation of the ARMin Rehabilitation Robot

Robot-assisted arm therapy is increasingly applied in neurorehabilitation. The reason for this development over the last decades was that robots relieve the therapist from hard physical work while the training intensity can be increased. Importantly, an increase in training intensity is closely linked to functional improvements of the patient. However, usability of the robot for therapists was hardly considered an important factor in rehabilitation robot development so far. We believe that usability of the robot is a key factor for acceptance of the device by therapists. In this paper, an online adaptive compensation for the ARMin rehabilitation robot is presented, which aims at improving usability of the robot. Therefore, we expect ARMin therapy to become even more effective than conventional therapy at a level that is also relevant for the patient. Additionally, improved usability relieves the therapist from unnecessary/time-consuming tasks linked to robot handling. For the ARMin, the new online adaptive compensation takes over automatic updates of changed upper and lower arm lengths as well as adaptation of shoulder angle settings to fit the patients anthropometry. Simultaneously, the model-based compensation in ARMin is directly updated to account for hardware changes. Importantly, the online adaptive compensation provides improved performance of ARMin even at the borders of the workspace. In experiments, we could show that the adaptive online compensation relieves the force and position controller from additional burdens and increases the robot performance drastically especially at the workspace border.

Notch filter and MPC for powered wheelchair operation under Parkinson's tremor

This paper considers a model predictive control (MPC) strategy for mitigating the effects of Parkinson tremors on a movement-sensing, joystick controlled battery powered wheelchair with regenerative braking to extend its range between charges. Regenerative braking transforms the wheelchair model into a (switched) hybrid system. The wheelchair is represented as a joystick controlled wheeled mobile robot (WMR) having four modes of operation, propelling and regenerative braking for each wheel. The joystick is presumed to provide velocity, orientation, and position commands. To enhance safety, velocity and acceleration saturation limits are imposed as constraints on the control activation. The paper delineates a notch filter to remove the main Parkinsons tremor followed by a model predictive control strategy to track velocity, orientation, and distance to a wall commands from the joystick. Results show significant feasible advantages for safe wheelchair operation by Parkinsons patients with tremor.

Improving Usability of Rehabilitation Robots: Hand Module Evaluation of the ARMin Exoskeleton

The impact of arm rehabilitation robots increases with their usability. Hereby, usability can refer to many aspects of the robot’s functionalities in relation to interaction with the patient and/or the therapist. In the current case, the usability of robotic hand modules are in the focus. Especially for patients with spastic hand function, the design of the hand module is a critical factor for the patient set-up time. In this paper, the development of a new hand module for the ARMin according to usability requirements is presented. The requirements entail fast set-up time, functional movement and force training as well as hygiene factors. The developed hand module fulfills the requirements and is expected to increase usability and acceptance of the device.

Feedforward model based arm weight compensation with the rehabilitation robot ARMin

Highly impaired stroke patients at early stages of recovery are unable to generate enough muscle force to lift the weight of their own arm. Accordingly, task-related training is strongly limited or even impossible. However, as soon as partial or full arm weight support is provided, patients are enabled to perform arm rehabilitation training again throughout an increased workspace. In the literature, the current solutions for providing arm weight support are mostly mechanical. These systems have components that restrict the freedom of movement or entail additional disturbances. A scalable weight compensation for upper and lower arm that is online adjustable as well as generalizable to any robotic system is necessary. In this paper, a model-based feedforward weight compensation of upper and lower arm fulfilling these requirements is introduced. The proposed method is tested with the upper extremity rehabilitation robot ARMin V, but can be applied in any other actuated exoskeleton system. Experimental results were verified using EMG measurements. These results revealed that the proposed weight compensation reduces the effort of the subjects to 26% on average and more importantly throughout the entire workspace of the robot.