Monitoring Gait Complexity in the Wild

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dc.contributor.advisorGage, William
dc.contributor.authorDi Bacco, Vincenzo Edoardo
dc.date.accessioned2023-12-08T14:25:14Z
dc.date.available2023-12-08T14:25:14Z
dc.date.issued2023-12-08
dc.date.updated2023-12-08T14:25:13Z
dc.degree.disciplineKinesiology & Health Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractHuman steady-state gait exhibits subtle stride-to-stride fluctuations that produce complex patterns, termed gait complexity, which provide information about the quality of gait control and adaptive capacity of the walker. Our knowledge of gait complexity is premised on studies estimating nonlinear measures such as fractal indices and entropy within controlled walking environments, which may misrepresent gait patterns. Such measures may be more sensitive compared to traditional linear gait variability measures (e.g., stride time coefficient of variation) for detecting age-related changes in gait control and fall risk. Although wearable sensors have demonstrated valid estimates of traditional gait measures while walking in the free-living environment, research on the estimate of gait complexity is scarce. Furthermore, current wearable sensors require secure attachment to specific body locations and must be given to participants which may not be feasible for long-term monitoring. Smartphones with embedded accelerometers may be a viable option for measuring gait complexity beyond the confines of the laboratory setting and provide a true representation of gait dynamics. Through a series of three studies, this dissertation was designed for the validation and implementation of a smartphone accelerometer system (SPAcc) to capture and compare gait dynamics during free-living walking among healthy young and older adults. Study 1 and Study 2 revealed that the SPAcc provides valid and reliable estimates of linear and nonlinear gait variability measures, similar to research-grade laboratory equipment, during both treadmill (Study 1) and overground (Study 2) walking while simply placed in the user’s front pants pocket. Study 3 utilized the SPAcc to capture walking bouts throughout a two-hour free-living walking condition among young and older adults. The SPAcc-derived measures revealed greater gait variability among older, compared to young adults. Gait complexity was found to be similar between age groups, with values greater than typical laboratory-based studies, which may suggest a more structured but adaptive stepping-strategy response due to the increased challenge associated with free-living walking. Overall, the SPAcc may be a low-cost, user-friendly, and viable option for remote monitoring of gait complexity in the free-living environment. Further work will need to determine what aspects of the environment influence gait complexity.
dc.identifier.urihttps://hdl.handle.net/10315/41617
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectBiomechanics
dc.subject.keywordsComplexity science
dc.subject.keywordsEntropy
dc.subject.keywordsFractals
dc.subject.keywordsGait
dc.subject.keywordsNonlinear dynamics
dc.subject.keywordsVariability
dc.subject.keywordsAdaptability
dc.subject.keywordsHealth sciences
dc.subject.keywordsMotor control
dc.subject.keywordsConcurrent validity
dc.subject.keywordsTest-retest reliability
dc.subject.keywordsWearable sensors
dc.subject.keywordsSmartphone accelerometer
dc.subject.keywordsFree-living walking
dc.subject.keywordsAging
dc.subject.keywordsFall risk
dc.titleMonitoring Gait Complexity in the Wild
dc.typeElectronic Thesis or Dissertation

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