A Flexible Architecture to Enhance Wearable Robots: Integration of EMG-informed Models
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Research on wearable robotic devices is fostered by the need to assist and restore human locomotion, which is essential for most daily life activities. Despite a continuous technological advancement, many challenges are to be faced before establishing the use of these devices as part of the rehabilitation process. A main concern is how to actively engage the users and monitor how they are affected by the exoskeleton. Basic analyses, such as measuring walking speed, must be extended to include analysis on balance, brain activity, and evaluation of neuromechanical effects. This scenario requires the use of different distributed sensor devices that must be carefully synchronized. This work describes the architecture we implemented to extend the capabilities of the H2 exoskeleton. As a proof of the architecture validity, we show the online estimation of users' muscle forces through the acquisition of electromyographic signals, that drive neuromusculoskeletal models. The proposed framework, built upon the Robot Operating System (ROS), aims to be reusable for a wide range of setups, including different exoskeletons and sensors.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
Artificial Intelligence and Image Processing not elsewhere classified