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Towards Robot-aided Gait Rehabilitat...

Rezayat Sorkhabadi, Seyed Mostafa,

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Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion // Seyed Mostafa Rezayat Sorkhabadi.
作者:	Rezayat Sorkhabadi, Seyed Mostafa,
面頁冊數:	1 electronic resource (145 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30810960
ISBN:	9798381157796

Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion /
Rezayat Sorkhabadi, Seyed Mostafa,

Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion /Seyed Mostafa Rezayat Sorkhabadi. - 1 electronic resource (145 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.

English

ISBN: 9798381157796Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Gait training

Towards Robot-aided Gait Rehabilitation and Assistance via Characterization and Estimation of Human Locomotion /
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520 $a Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
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590 $a School code: 0010
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650 4 $a Robotics. $3 519753
650 4 $a Biomechanics. $3 548685
650 4 $a Biomedical engineering. $3 535387
653 $a Gait training
653 $a Learning from demonstration
653 $a State estimation
653 $a Wearable robotics
653 $a Gait assistance
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690 $a 0771
690 $a 0648
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710 2 $a Arizona State University. $b Mechanical Engineering. $e degree granting institution. $3 3821415
720 1 $a Zhang, Wenlong $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0010
791 $a Ph.D.
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