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You searched for subject:(stance phase). Showing records 1 – 3 of 3 total matches.

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University of Toronto

1. Wyss, Dominik. Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism.

Degree: 2012, University of Toronto

A rear locking prosthetic knee joint with a durable, rear Automatic Stance-Phase Lock (ASPL), was developed to investigate the versatility of the (ASPL) mechanism in improving the functionality of prosthetic knees appropriate for a global market. An international survey and a Quality Function Deployment identified deficits with existing prosthetic knee mechanisms and established the most influential design parameters. Work on the knee design was completed following a comparative stability analysis of different knee mechanisms which justified the initial design. Solid models were generated with computer design software and a prototype was produced and structurally tested. Finally, clinical pilot testing was conducted on a unilateral transfemoral amputee, and various gait variables were assessed. As hypothesized, the knee performed close to the level of a conventional six-bar knee providing highly effective stance-phase control and the pilot test showed that improvements to the swing-phase response could further reduce the asymmetry of gait.

MAST

Advisors/Committee Members: Cleghorn, William L., Andrysek, Jan, Mechanical and Industrial Engineering.

Subjects/Keywords: Prosthetic; Stance-phase; Knee; Stability; Amputee; Joint; 0548; 0541

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wyss, D. (2012). Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/33575

Chicago Manual of Style (16th Edition):

Wyss, Dominik. “Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism.” 2012. Masters Thesis, University of Toronto. Accessed April 11, 2021. http://hdl.handle.net/1807/33575.

MLA Handbook (7th Edition):

Wyss, Dominik. “Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism.” 2012. Web. 11 Apr 2021.

Vancouver:

Wyss D. Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism. [Internet] [Masters thesis]. University of Toronto; 2012. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/1807/33575.

Council of Science Editors:

Wyss D. Evaluation and Design of a Globally Applicable Rear-locking Prosthetic Knee Mechanism. [Masters Thesis]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/33575


Iowa State University

2. Taghavi, Nazita. A device for sensing and balance augmentation using functional electrical stimulation.

Degree: 2020, Iowa State University

Based on World Health Organization (WHO) report, between 250,000 and 500,000 people suffer from disabilities caused by spinal cord injuries each year. The result of this study is development of a medical device to restore walking in such patients using Functional Electrical Stimulation (FES). We selected dogs as our animal subject. This device uses FES to prevent an affected dog with limited walking abilities from falling during walking. The final version of the device includes a sensing core consisted of four Inertial Measurement Units (IMUs) attached to the hip, femur, tibia and metatarsus of our test subject. Using this sensory system, the device tracks and measures the hip, knee and hock joint angles in real time. We use a commercial microcontroller as our analytical core to provide suitable stimulation commands and provide appropriate voltage/current for delivery to target muscles. Data from IMUs are received by microcontroller using I2C bus communication. An advanced embedded C code is developed to program the microcontroller. We discuss a method to recognize the swing and stance phases of the dog gait during walking and propose several balancing strategies to be used for gait control during the stance and swing phase before falling occurs. We design and build a robodog to be compatible with the medical device. We use this robot to program and test the different cores of the device. We test our balancing strategies on our bionic test-bed before applying them on an actual animal subject. Results show the device can provide suitable sensing and stimulation control to balance the body of a dog that has limited ambulation abilities.

Subjects/Keywords: functional electrical stimulation; gait; spinal cord injuries; stance phase; swing phase; wearable body balancing device

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Taghavi, N. (2020). A device for sensing and balance augmentation using functional electrical stimulation. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17862

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Taghavi, Nazita. “A device for sensing and balance augmentation using functional electrical stimulation.” 2020. Thesis, Iowa State University. Accessed April 11, 2021. https://lib.dr.iastate.edu/etd/17862.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Taghavi, Nazita. “A device for sensing and balance augmentation using functional electrical stimulation.” 2020. Web. 11 Apr 2021.

Vancouver:

Taghavi N. A device for sensing and balance augmentation using functional electrical stimulation. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Apr 11]. Available from: https://lib.dr.iastate.edu/etd/17862.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Taghavi N. A device for sensing and balance augmentation using functional electrical stimulation. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/17862

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Farah, John. Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control .

Degree: 2018, University of Ottawa

Microprocessor-controlled stance-control knee-ankle-foot orthoses (M-SCKAFO) can have multiple sensors at all lower-limb segments. This causes M-SCKAFO to be bulky and expensive, with complex control systems. Stance-control systems with sensors local to the knee-joint component would provide a modular orthosis component for easier orthotist-customization and personalization for users with knee-extensor weakness. A gait phase recognition model (GPR) is essential for a fast, accurate, and generalizable real-time orthosis-control. This thesis designed, developed, and evaluated a machine learning-based GPR model for intelligent M-SCKAFO control. The model used gait signals that mimicked thigh inertial sensor and knee angle. Machine learning was implemented to identify gait phases across multiple surface conditions and walking speeds. Thigh-segment angular velocity, thigh-segment acceleration, and knee angle were calculated from 30 able-bodied participants for level and up, down, right-cross, and left-cross slopes at 0.8, 0.6, 0.4 m/s, and self-paced speeds (1.33 m/s, SD = 0.04 m/s). A logistic model tree (LMT) was built with a set of 20 signal features extracted from 0.1s sliding windows. The GPR model determined the walking state and was fed through a “transition sequence verification and correction” (TSVC) algorithm to deal with continuous states. The GPR model was evaluated on a different data set from 12 able-bodied individuals that completed the same walking protocol (validation set). Gait phases were classified successfully regardless of surface-level, walking speed, and individual walking variability. The LMT had a tree size of 1643 nodes with 822 leaf nodes. The GPR model produced overall classification accuracy of 98.4% and increased to 98.7% when TSVC was applied. Results also demonstrated evidence of strong model-generalizability with GPR accuracy of 90.6% and increased to 98.6% when TSVC was applied, on the validation set. This research demonstrated that local sensor signals from thigh and knee, integrated with machine intelligence algorithms, provided viable GPR suitable for real-time orthosis-control. The logistic decision tree model and feature selection approach were computationally efficient for real-time GPR and gave reliable, robust, and generalizable results across multiple surfaces, walking speeds, and individual walking variability. GPR also benefitted from transition sequence verification and correction algorithms, providing enhanced gait phase classification performance.

Subjects/Keywords: Artificial Intelligence; Machine Learning; Gait; Stance Control; Knee ankle Foot Orthosis; Orthosis; Control System; Inertial Measurement Unit; Gait Phase Recognition; Microprocessor; Sensors; Feature Selection

…and pressure sensors to achieve gait phase recognition and stance-control. Rule-based and… …Swing phase is the period after stance phase when the foot leaves the ground and swings… …extensors) is required to stabilize and support an individual’s weight during stance phase… …stance/swing phase recognition leading to unreliable locking and less functional versatility… …During mid-stance phase the artificial extensors initiated knee extension and the flexors began… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Farah, J. (2018). Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/37730

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Farah, John. “Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control .” 2018. Thesis, University of Ottawa. Accessed April 11, 2021. http://hdl.handle.net/10393/37730.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Farah, John. “Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control .” 2018. Web. 11 Apr 2021.

Vancouver:

Farah J. Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control . [Internet] [Thesis]. University of Ottawa; 2018. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/10393/37730.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Farah J. Design and Implementation of an Artificial Intelligence-Driven Gait Phase Recognition System for Orthotic Knee Control . [Thesis]. University of Ottawa; 2018. Available from: http://hdl.handle.net/10393/37730

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.