  Soft wearable robots are a promising new design paradigm for rehabilitation
and active assistance applications. Their compliant nature makes them ideal for
complex joints like the shoulder, but intuitive control of these robots require
robust and compliant sensing mechanisms. In this work, we introduce the sensing
framework for a multi-DoF shoulder exosuit capable of sensing the kinematics of
the shoulder joint. The proposed tendon-based sensing system is inspired by the
concept of muscle synergies, the body's sense of proprioception, and finds its
basis in the organization of the muscles responsible for shoulder movements. A
motion-capture-based evaluation of the developed sensing system showed
conformance to the behaviour exhibited by the muscles that inspired its routing
and validates the hypothesis of the tendon-routing to be extended to the
actuation framework of the exosuit in the future. The mapping from multi-sensor
space to joint space is a multivariate multiple regression problem and was
derived using an Artificial Neural Network (ANN). The sensing framework was
tested with a motion-tracking system and achieved performance with root mean
square error (RMSE) of approximately 5.43 degrees and 3.65 degrees for the
azimuth and elevation joint angles, respectively, measured over 29000 frames
(4+ minutes) of motion-capture data.
