Soft Robotic Glove with Integrated Sensing for Intuitive Grasping Assistance Post Spinal Cord Injury
| Autor: | Ryan Solinsky, Roman Heimgartner, Kristin Nuckols, Megan E. Clarke, Conor J. Walsh, Sabrina Paganoni, Dorothy Orzel, Carolina Correia, Diana Wagner, Yu Meng Zhou, Ciaran T. O'Neill |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science GRASP Soft robotics 02 engineering and technology 021001 nanoscience & nanotechnology medicine.disease body regions 020901 industrial engineering & automation Control theory User control medicine 0210 nano-technology Actuator Spinal cord injury Simulation |
| Zdroj: | ICRA |
| Popis: | This paper presents a fully-integrated soft robotic glove with multi-articular textile actuators, custom soft sensors, and an intuitive state machine intent detection controller. We demonstrate that the pressurized actuators can generate motion and force comparable to natural human fingers through bench-top testing. We apply textile-elastomer capacitive sensors to the glove to track finger flexion via strain and detect contact with objects via force. Intuitive user control is achieved via a state machine controller based on signals from the integrated sensors to detect relative changes in hand-object interactions. Results from an initial evaluation with 3 participants with spinal cord injury (SCI), of varied injury levels and years since injury, wearing and controlling the glove show an average of 87% improvement in grasping force, and improvements in functional assessments for participants with recent injuries. A significant variation in response suggests further investigation is required to understand the adaptation needed across different injury levels and durations since injury. Additionally, we evaluate the controller and find an average of 3 seconds from user initiations to completed grasps, and 10% inadvertent grasp triggers and no false releases when objects are held. |
| Databáze: | OpenAIRE |
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