Walk this way : real-time gait retraining as a conservative treatment for knee osteoarthritis
Abstract/Contents
- Abstract
- Haptic feedback stimulates the skin and is useful for conveying information across the human body. Combining haptic feedback with movement sensing and computation in real-time can create a rich and diverse system for characterizing and retraining human movement. This thesis presents haptic gait retraining as a conservative treatment for individuals with medial compartment knee osteoarthritis. Wearable haptic feedback devices, which either vibrate or stretch the skin, are worn on the body and used to inform kinematic changes during walking. The focus of gait retraining is to shift loading away from the arthritic medial compartment of the knee by reducing the knee adduction moment - a clinically relevant measure that has been linked to pain, incidence, severity, and progression of medial compartment knee osteoathritis. Thus, training individuals with knee osteoarthritis to walk in a way that reduces the knee adduction moment has the potential to reduce knee pain, improve function, and slow osteoarthritis progression. Four experiments were conducted to explore the viability of haptic gait retraining. During all experiments, subjects walked on an instrumented treadmill while motion capture data was streamed in real-time and wearable haptic feedback was used to inform gait changes. In the first experiment, subjects received feedback of the knee adduction moment measurement and were given freedom to make kinematic changes to lower the knee adduction moment. All subjects demonstrated the ability to make gait modifications to acheive this goal, though some chose awkward gait patterns. In the second experiment, haptic feedback was used to inform changes to the foot progression angle as subjects were trained to ``toe-in'' more than they normally did. Toe-in gait proved to be an easy-to-learn gait pattern which reduced the knee adduction moment and looked natural. The purpose of the third experiment was to train simultaneous changes to multiple kinematic gait parameters. Subjects received haptic feedback for trunk sway, tibia, and foot progression angles to inform new gait patterns based on correlations between the knee adduction moment and each kinematic parameter. Subjects demonstrated large reductions in the knee adduction moment comparable to more invasive surgical interventions such as high tibial osteotomy. In the final experiment individuals with persistent knee pain and clinically diagnosed knee osteoarthritis participated in a six week gait retraining program to assess learning retention and symptom changes. Subjects demonstrated learning retention and at the end of the six weeks reported improvements in knee pain and function, which were significantly greater than from the expected placebo effect. This thesis shows that haptic gait retraining is a promising conservative treatment option for individuals suffering from medial compartment knee osteoarthritis. Haptic gait retraining has the potential to alter walking patterns which could over time reduce knee pain, slow osteoarthritis progression, and ultimately improve quality of life.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Shull, Pete Bradley |
|---|---|
| Associated with | Stanford University, Department of Mechanical Engineering |
| Primary advisor | Cutkosky, Mark R |
| Thesis advisor | Cutkosky, Mark R |
| Thesis advisor | Besier, Thor |
| Thesis advisor | Delp, Scott |
| Advisor | Besier, Thor |
| Advisor | Delp, Scott |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Pete B. Shull. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by Pete Bradley Shull
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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