2 research outputs found
Design and Prototyping of a Bio-inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit
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.Comment: 8 pages, 7 figures, 1 tabl
Nonlinearity Compensation in a Multi-DoF Shoulder Sensing Exosuit for Real-Time Teleoperation
The compliant nature of soft wearable robots makes them ideal for complex
multiple degrees of freedom (DoF) joints, but also introduce additional
structural nonlinearities. Intuitive control of these wearable robots requires
robust sensing to overcome the inherent nonlinearities. This paper presents a
joint kinematics estimator for a bio-inspired multi-DoF shoulder exosuit
capable of compensating the encountered nonlinearities. To overcome the
nonlinearities and hysteresis inherent to the soft and compliant nature of the
suit, we developed a deep learning-based method to map the sensor data to the
joint space. The experimental results show that the new learning-based
framework outperforms recent state-of-the-art methods by a large margin while
achieving 12ms inference time using only a GPU-based edge-computing device. The
effectiveness of our combined exosuit and learning framework is demonstrated
through real-time teleoperation with a simulated NAO humanoid robot.Comment: 8 pages, 7 figures, 3 tables. Accepted to be published in IEEE
RoboSoft 202