Trajectory Learning by Therapists' Demonstrations for an Upper Limb Rehabilitation Exoskeleton

In this work, we propose a method for trajectory implementation based on the Learning by Demonstrations approach to deal with trajectory planning issues in upper-limb rehabilitation exoskeletons. Currently applied path-planning methods use mathematical trajectories or Teach-and-play approaches. The former do not propose human-like movements to patients, which is crucial to induce correct motor relearning. Moreover, they often differ from therapists' expectations of how movements should be executed, reducing the acceptability and use of exoskeletons in hospitals. The latter, using a single filtered trajectory demonstration, better meet therapists' expectations but lack consistency and optimization. In our approach, we employed Hidden Markov Models, still never used for rehabilitation robotics, to study a set of demonstrations and we optimized the results to respect physiological muscular activation patterns. Recorded few repetitions of a movement from the interaction of a therapist with an exoskeleton, our machine-learning-based algorithm returns a ready-to-use trajectory representing the therapist's desires. We tested our method on a 4 degrees-of-freedom exoskeleton to record 5 exercises, interacting with 5 therapists. Comparing our trajectories with those obtained with literature methods, we see that our approach produces better kinematic and human-likeness results, and is better according to the global opinion expressed by the therapists.