Series Elastic Actuation: Facilitating Robotic Assessment of Human Neurological and Biomechanical Properties
| dc.contributor.advisor | O'Malley, Marcia K | |
| dc.creator | Erwin, Andrew | |
| dc.date.accessioned | 2019-05-17T15:05:00Z | |
| dc.date.available | 2019-05-17T15:05:00Z | |
| dc.date.created | 2018-05 | |
| dc.date.issued | 2018-04-20 | |
| dc.date.submitted | May 2018 | |
| dc.date.updated | 2019-05-17T15:05:00Z | |
| dc.description.abstract | This thesis proposes integrating series elastic actuators into the design of robots that facilitate assessment of human neurological function and biomechanical properties. Such assessments can provide objective measures of the physiological adaptations which promote recovery after neurological injury, such as stroke or spinal cord injury. These adaptations are currently not well-understood, leading to variable recovery despite intensive rehabilitation. The robot-aided assessments reported in this thesis can be used to identify the neurological catalysts for brain plasticity and the biomechanical factors for everyday function essential for motor recovery. Robots currently used for these assessments do not measure torque and so use backdrivable (i.e., low friction) actuators since nonbackdrivable (i.e., high friction) actuators cannot be used for accurate torque control. However, accurate torque control with nonbackdrivable actuators can be achieved through series elastic actuation, a design in which an elastic element is purposefully incorporated in series between the actuator and user. To overcome limitations found in existing backdrivable robots, this thesis develops two novel series elastic actuated assessment robots with nonbackdrivable actuators for applications in robot-aided assessment. In the first application, brain activity is acquired through functional magnetic resonance imaging during physical human-robot interaction with the Magnetic Resonance Soft Wrist (MR-SoftWrist) robot. The MR- SoftWrist features series elastic actuation to overcome limitations in using non-ferrous and nonbackdrivable ultrasonic motors. As demonstrated in this thesis, the MR-SoftWrist can interact with the wrist safely and accurately during functional magnetic resonance imaging, while not degrading acquired images of brain activity. In the second application, passive stiffness and active range of motion wrist envelopes are assessed with the novel Series Elastic Assessment Wrist (SE-AssessWrist) exoskeleton. The SE-AssessWrist employs rotary series elastic actuators to provide accurate torque estimation and zero force control, despite using nonbackdrivable actuators consisting of geared motors and a flexible Bowden cable transmission. The SE-AssessWrist can facilitate measurement of wrist biomechanics, in particular determination of the axis of least stiffness and greatest range of motion, which does not coincide with anatomical axes. As an important first step towards improved robotic rehabilitation, the proposed robot-aided assessments in this thesis are validated in able-bodied human experiments. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Erwin, Andrew. "Series Elastic Actuation: Facilitating Robotic Assessment of Human Neurological and Biomechanical Properties." (2018) Diss., Rice University. <a href="https://hdl.handle.net/1911/105742">https://hdl.handle.net/1911/105742</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/105742 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | series elastic actuators | |
| dc.subject | force sensing | |
| dc.subject | functional magnetic resonance imaging | |
| dc.subject | MR-compatible robotics | |
| dc.subject | robot-aided assessment | |
| dc.title | Series Elastic Actuation: Facilitating Robotic Assessment of Human Neurological and Biomechanical Properties | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Mechanical Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
Files
Original bundle
1 - 1 of 1