For haptic devices, compensation of the robot's gravity is a frequent strategy with the aim to reduce interaction forces between robot and human in zero-impedance control. However, a closer look at the composition of these interaction forces may reveal that the net effect of uncompensated gravitational components of the robot actually reduces interaction forces during dynamic movements, because inertial and gravitational components at least partially compensate each other. This is the case in lower extremity exoskeletons, where less user force is necessary to swing the robot's leg when gravity helps. Here, we go one step further by shaping optimal passive dynamics for arbitrary haptic devices. The proposed method of generalized elasticities uses conservative force fields to improve haptic transparency for certain movements types. In an example realization, these force fields are generated by elasticities spanning multiple joints. Practical experiments with the Lokomat lower extremity exoskeleton show the success of the proposed method in terms of reduced interaction torques and more physiological user motion compared to gravity compensation.

The rehabilitation robot Lokomat allows automated treadmill training for patients with neurological gait disorders. The basic position control approach for the robot has been extended to patient-cooperative strategies. These strategies provide more freedom and allow patients to actively influence their training. However, patients are likely to need additional support during patient-cooperative training. In this paper, we propose an algorithm based on iterative learning control that shapes a supportive torque field. The torque field is supposed to assist the patient as much as needed in performing the desired task. We evaluated the algorithm in a proof-of-concept experiment with 3 healthy subjects. Results showed that the amount of support was automatically adapted to the activity and the individual needs of the subjects. Furthermore, the support improved the performance of the subjects.

The rehabilitation robot LOKOMAT has been developed at University Hospital Balgrist to automate treadmill training of spinal cord injury and stroke patients. Current rehabilitation training on that robot consists of moving the patient's legs on predefined trajectories. However, this kind of training is not challenging, as patients are moved regardless of their efforts and do not see their advancement. To enhance rehabilitation training with the LOKOMAT, a virtual reality setup was installed. It consists of a passive stereo projection system (screen size 3 m times 2 m), a Dolby 5.1 sound system and an electric fan. With that setup an obstacle crossing scenario was implemented. The patients can see their advancement on the screen, as an animated figurine (avatar) moves along a path simultaneously with their own movements. Additionally they can hear sounds (e.g. environmental sounds, steps), feel the wind, and experience force feedback, provided by the orthosis, when hitting obstacles. The objective of a first study on visual feedback was to investigate which feedback suits best to perceive the obstacle distances and heights correctly. To answer this question, 14 healthy subjects walked in the actuated gait orthosis, received visual feedback and tried to avoid collisions with obstacles. Subjects could move freely within the gait orthosis and determine their own speed and step length. They had to cross the obstacles independently, with haptic feedback, indicating obstacle hits. Results show that the side view results in least obstacle hits and that 2D excels 3D display in this respect