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dc.contributorDepartment of Biomedical Engineeringen_US
dc.contributor.advisorZhang, Ming (BME)en_US
dc.creatorZhang, Guoxin-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/12292-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleInvestigation of gait parameters for fatigue detectionen_US
dcterms.abstractFatigue 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.en_US
dcterms.abstractThis 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.en_US
dcterms.abstractIn 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.en_US
dcterms.abstractIn 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).en_US
dcterms.abstractIn 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.en_US
dcterms.extentxxv, 170 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2023en_US
dcterms.educationalLevelPh.D.en_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHFootwear -- Designen_US
dcterms.LCSHMuscles -- Physiologyen_US
dcterms.LCSHFatigueen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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