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 ≈ 5.43 • and ≈ 3.65 • for the azimuth and elevation joint angles, respectively, measured over 29,000 frames (4+ minutes) of motion-capture data.

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Abstract-Advances in optical imaging, and probe-based Confocal Laser Endomicroscopy (pCLE) in particular, offer real-time cellular level information for in-vivo tissue characterization. However for large area coverage, the limited field-of-view necessitates the use of a technique known as mosaicking to generate usable information from the incoming image stream. Mosaicking also needs a continuous stream of good quality images, but this is challenging as the probe needs to be maintained within an optimal working range and the contact force controlled to minimize tissue deformation. Robotic manipulation presents a potential solution to these challenges, but the lack of haptic feedback in current surgical robot systems hinders the technology's clinical adoption. This paper proposes a sensorless alternative based on processing the incoming image stream and deriving a quantitative measure representative of the image quality. This measure is then used by a controller, designed using model-free reinforcement learning techniques, to maintain optimal contact autonomously. The developed controller has shown near real-time performance in overcoming typical loss-of-contact and excess-deformation scenarios experienced during endomicroscopy scanning procedures.

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