| Author: | Zhang, Guoxin |
| Title: | Investigation of gait parameters for fatigue detection |
| Advisors: | Zhang, Ming (BME) |
| Degree: | Ph.D. |
| Year: | 2023 |
| Subject: | Footwear -- Design Muscles -- Physiology Fatigue Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Biomedical Engineering |
| Pages: | xxv, 170 pages : color illustrations |
| Language: | English |
| Abstract: | Fatigue of the lower-extremity muscles can induce gait instability and increases the risk of injury among older adults. Recognizing muscle fatigue can facilitate a timely response and management of older adults during physical exercise and thus reduce the risk of injury. This study aimed to develop a footwear system that can assess muscle fatigue-related gait parameters and adjust the medial arch support height and insole stiffness accordingly to relieve pronation and improve the balance control ability. This project consisted of three parts. The first part aimed to identify the determinants of gait parameters in response to muscle fatigue, which was induced by long-time fast walking in our study. Eighteen older participants were recruited for a 60-minute treadmill walking trial. Muscle fatigue, as verified by the Borg scale and surface electromyography, increased the mediolateral acceleration, the maximum rotation angle, and the range of rotation angles on the coronal and transverse planes of the posterior heel region; and shifted the plantar load medially in the rearfoot and midfoot regions and laterally in the forefoot regions. In the second part, a footwear system was developed that can measure plantar load and heel kinematics and endeavor to classify the fatigue state using a machine learning model. The system consisted of a microcontroller unit (MCU) with a Bluetooth chip, seven force sensors, and a single inertial measurement unit (IMU). The IMU was attached to the posterior heel region of the right shoe. The data from 18 older adults conducting brisk treadmill walking for 60 minutes were collected. The machine learning model (Support Vector Machine, SVM) classified the fatigue and non-fatigue states by the first and last five minutes of the brisk walking data. A leave-one-out cross-validation approach was adopted. The sensitivity, specificity, and accuracy were all 98%. In addition, it revealed that all participants had a 91% chance of reaching the fatigue state after brisk walking for 40 minutes. Moreover, a smartphone application with a user interface was developed to facilitate future outdoor assessment. In the third part, the variable arch support height and stiffness components were incorporated into the insole through the magnetorheological fluid (MRF). An actuator changes the position of a magnet and adjusts the thickness and stiffness of the MRF sacs. The system was activated on the same participants briskly walking on the treadmill at the 40th minute. Compared to the control group (non-activation of the system), the participants reported a significantly higher level of mediolateral control perception and heel cushioning, gauged by a visual analog scale (p = 0.047; p = 0.049) and measured plantar force in the medial arch (p = 0.015). In summary, this study developed an innovative footwear system that can adjust the medial arch support height and the stiffness of the insole upon muscle fatigue in real-time. The results could provide a better understanding of the association of muscle fatigue with gait parameters. This footwear system may potentially reduce the risk of injury and help older adults overcome their fear of injury, so that they can exercise more, improve their quality of life, and reduce their medical costs substantially. |
| Rights: | All rights reserved |
| Access: | open access |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/12292