Robot-assisted therapy has become increasingly popular and useful in post-stroke neurorehabilitation. This paper presents an overview of the design and control of the dual-arm Recupera exoskeleton to provide intense therapist-guided as well as self training for sensorimotor rehabilitation of the upper body. The exoskeleton features a lightweight design, high level of modularity, decentralized computing, and various levels of safety implementation. Due to its modularity, the system can be used as a wheel-chair mounted system or as a full-body system. Both systems enable a wide range of therapies while efficiently grounding the weight of the system and without compromising the patient’s mobility. Furthermore, two rehabilitation therapies implemented on the exoskeleton system, namely teach & replay therapy and mirror therapy, are presented along with experimental results.

Performance of fine motor tasks during the initial phase of space missions is often compromised by the adaptation to microgravity. Since traditional Earth-based training methods are limited and struggle to replicate these conditions without strict time constraints, we propose the training of fine motor tasks with simulated microgravity on earth using an upper limb active exoskeleton. With a model-based control approach, we create a state of microgravity for both arms. To enable realistic microgravity simulation, a suitable model of the human arm is needed. We developed a method to identify the parameters of an arm model by leveraging the computational graph of the inverse dynamics algorithm and utilizing gradient descent to minimize the discrepancy between model and reality. Preliminary data from parabolic flights show that subjects trained with our exoskeleton achieved higher accuracy in a fine motor task during their first exposure to real microgravity compared to untrained subjects.