Design and Prototyping of a Bio-inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit

TL;DR

This paper presents a novel tendon-based sensing system inspired by muscle synergies for a multi-DoF shoulder exosuit, demonstrating accurate joint angle estimation and paving the way for intuitive control of soft wearable robots.

Contribution

The work introduces a bio-inspired tendon routing and an ANN-based mapping for shoulder joint sensing in a soft exosuit, advancing proprioceptive sensing technology.

Findings

Achieved RMSE of ~5.43° for azimuth and ~3.65° for elevation angles.

Validated tendon routing through motion-capture data.

Demonstrated potential for extension to actuation control.

Abstract

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.