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Human Prediction and Robotic Lower-Limb Prosthesis Planning for Safe Perturbation Recovery during Motion.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Human Prediction and Robotic Lower-Limb Prosthesis Planning for Safe Perturbation Recovery during Motion./
作者:	Danforth, Shannon M.
面頁冊數:	1 online resource (180 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Biomechanics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29730294click for full text (PQDT)
ISBN:	9798845456274

Human Prediction and Robotic Lower-Limb Prosthesis Planning for Safe Perturbation Recovery during Motion.
Danforth, Shannon M.

Human Prediction and Robotic Lower-Limb Prosthesis Planning for Safe Perturbation Recovery during Motion. - 1 online resource (180 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--University of Michigan, 2022.

Includes bibliographical references

Falls are prevalent among older adults and people with lower-limb amputation of all ages. One recommendation to reduce fall risk is to identify those who are most likely to fall and provide targeted physical therapy, but existing methods for identifying fall risk have been unable to reliably predict who will fall. Another way to reduce falls is to assist the individual using a wearable robotic device, such a prosthesis, when stumbles occur. However, because there is uncertainty in how the human will respond, wearable robots are unable to safely assist with trip recovery. To accurately identify who may become unstable during motion, this dissertation presents Stability Basins, which characterize individual dynamic stability during the Sit-to-Stand motion. Stability Basins, formed using a dynamic model with a model for the individual's control strategy, encompass all stable model states at each point during the motion. In this document, Stability Basins are validated using data from an 11-subject experiment where subjects were pulled by motor-driven cables as they stood up from a chair. The Stability Basins' accurate characterization of stability during motion shows promise for identifying fall risk. Another way to predict human response to perturbation (e.g., a push or trip) is to find an individual's underlying objective during motion and use it to inform predictions. Given the assumption that humans are optimizing some cost such as metabolic energy during motion, inverse optimal control finds the underlying objective function corresponding to observed data. This dissertation presents results from applying an inverse optimal control formulation to the 11-subject Sit-to-Stand dataset. Results suggest that subjects place priority on the position and velocity of their center of mass rather than input torques during both perturbed and unperturbed Sit-to-Stand, and that the underlying cost function can be used to effectively simulate perturbation response. Accurate and quick predictions of trip recovery during walking are necessary for planning safe trip-recovery motion in wearable robots. A 16-subject experiment was conducted where subjects were tripped via tethers attached to their feet while walking on a treadmill. In this document, three different models (Gaussian process regression, neural network, and dynamic model) were trained on the trip-recovery data and evaluated for prediction accuracy and computation time. The Gaussian process regression models outperformed the other two in both criteria, highlighting their potential for use in wearable robot trip-recovery planning. Despite the success of the Gaussian process regression models, predictions of human motion will never be perfectly accurate. To safely plan motion for trip recovery, wearable robots must account for prediction uncertainty. This dissertation presents a framework for planning trip-recovery motion in a robotic knee-ankle prosthesis after a trip occurs that accounts for a set of predicted human behavior. The framework solves an optimization problem to ensure that the foot is placed close to a desired location at the end of the step while avoiding unsafe foot scuffs before touchdown. The approach is demonstrated in simulation using data from the 16-subject trip experiment. In the final chapter, this dissertation argues that robotics researchers should conduct technology assessments to address factors that may influence the real-world adoption of a technology, and presents preliminary results from a technology assessment of robotic lower-limb prostheses. Together, the frameworks, tools, and technology assessment included in this dissertation advance the practical and safe use of autonomy to reduce falls and fall-related injuries.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845456274Subjects--Topical Terms:

548685
Biomechanics.
Subjects--Index Terms:

Assistive roboticsIndex Terms--Genre/Form:

542853
Electronic books.

Human Prediction and Robotic Lower-Limb Prosthesis Planning for Safe Perturbation Recovery during Motion.
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