Sensing and Control of a Multi-Joint Soft Wearable Robot for Upper-Limb Assistance and Rehabilitation
| Autor: | Cameron J. Hohimer, Megan E. Clarke, Conor J. Walsh, David Lin, Ciaran T. O'Neill, Kristin Nuckols, Yu Meng Zhou, Tommaso Proietti |
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| Rok vydání: | 2021 |
| Předmět: |
soft robotics
030506 rehabilitation 0209 industrial biotechnology Control and Optimization Activities of daily living wearable robotics Computer science medicine.medical_treatment Biomedical Engineering Wearable computer 02 engineering and technology Kinematics Motion capture 03 medical and health sciences 020901 industrial engineering & automation Wearable robot Artificial Intelligence Inertial measurement unit Control theory Rehabilitation robotics medicine Simulation Rehabilitation Mechanical Engineering Torso Computer Science Applications body regions Human-Computer Interaction medicine.anatomical_structure Control and Systems Engineering Trajectory Robot Computer Vision and Pattern Recognition 0305 other medical science |
| Zdroj: | IEEE Robotics and Automation Letters. 6:2381-2388 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2021.3061061 |
| Popis: | In the field of wearable robotics, there has been increased interest in the creation of soft wearable robots to provide assistance and rehabilitation for those with physical impairments. Compared to traditional robots, these devices have the potential to be fully portable and lightweight, a flexibility that may allow for increased utilization time as well as enable use outside of a clinical environment. In this letter, we present a textile-based multi-joint soft wearable robot to assist the upper limb, in particular shoulder elevation and elbow extension. Before developing a portable fluidic supply system, we leverage an off-board actuation system for power and control, with the worn components weighting less than half kilogram. We showed that this robot can be mechanically transparent when powered off, not restricting users from performing movements associated with activities of daily living. Three IMUs were placed on the torso, upper arm and forearm to measure the shoulder and elbow kinematics. We found an average RMSE of $\sim\!5$ degrees when compared to an optical motion capture system. We implemented dynamic Gravity Compensation (GC) and Joint Trajectory Tracking (JTT) controllers that actively modulated actuator pressure in response to IMU readings. The controller performances were evaluated in a study with eight healthy individuals. Using the GC controller, subject shoulder muscle activity decreased with increasing magnitude of assistance and for the JTT controller, we obtained low tracking errors (mean $\sim\!6$ degrees RMSE). Future work will evaluate the potential of the robot to assist with activities in post-stroke rehabilitation. |
| Databáze: | OpenAIRE |
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