Gait Variability for Predicting Individual Performance in Military-Relevant Tasks
| dc.contributor.author | Ulman, Sophia Marie | en |
| dc.contributor.committeechair | Nussbaum, Maury A. | en |
| dc.contributor.committeechair | Srinivasan, Divya | en |
| dc.contributor.committeemember | Madigan, Michael L. | en |
| dc.contributor.committeemember | Queen, Robin M. | en |
| dc.contributor.committeemember | Haynes, Courtney Ann | en |
| dc.contributor.department | Industrial and Systems Engineering | en |
| dc.date.accessioned | 2019-10-04T08:00:36Z | en |
| dc.date.available | 2019-10-04T08:00:36Z | en |
| dc.date.issued | 2019-10-03 | en |
| dc.description.abstract | Human movement is inherently complex, requiring the control and coordination of many neurophysiological and biomechanical degrees-of-freedom, and the extent to which individuals exhibit variation in their movement patterns is captured by the construct of motor variability (MV). MV is being used increasingly to describe movement quality and function among clinical populations and elderly individuals. However, current evidence presents conflicting views on whether increased MV offers benefits or is a hindrance to performance. To better understand the utility of MV for performance prediction, we focused on current research needs in the military domain. Dismounted soldiers, in particular, are expected to perform at a high level in complex environments and under demanding physical conditions. Hence, it is critical to understand what strategies allow soldiers to better adapt to fatigue and diverse environmental factors, and to develop predictive tools for estimating changes in soldier performance. Different aspects of performance such as motor learning, experience, and adaptability to fatigue were investigated when soldiers performed various gait tasks, and gait variability (GV) was quantified using four different types of measures (spatiotemporal, joint kinematics, detrended fluctuation analysis, and Lyapunov exponents). During a novel obstacle course task, we found that frontal plane coordination variability of the hip-knee and knee-ankle joint couples exhibited strong association with rate of learning the novel task, explaining 62% of the variance, and higher joint kinematic variability during the swing phase of baseline gait was associated with faster learning rate. In a load carriage task, GV measures were more sensitive than average gait measures in discriminating between experience and load condition: experienced cadets exhibited reduced GV (in spatiotemporal measures and joint kinematics) and lower long-term local dynamic stability at the ankle, compared to the novice group. In the final study investigating multiple measures of obstacle performance, and variables predictive of changes in performance following intense whole-body fatigue, joint kinematic variability of baseline gait explained 28-59% of the variance in individual performances changes. In summary, these results support the feasibility of anticipating and augmenting task performance based on individual motor variability. This work also provides guidelines for future research and the development of training programs specifically for improving military training, performance prediction, and performance enhancement. | en |
| dc.description.abstractgeneral | All people move with some level of inherent variability, even when doing the same activity, and the extent to which individuals exhibit variation in their movement patterns is captured by the construct of motor variability (MV). MV is being increasingly used to describe movement quality and function among clinical populations and elderly individuals. However, it is still unclear whether increased MV offers benefits or is a hindrance to performance. To better understand the utility of MV for performance prediction, we focused on current research needs in the military domain. Dismounted soldiers, in particular, are expected to perform at a high level in complex environments and under demanding physical conditions. Hence, it is critical to understand what strategies allow soldiers to better adapt to fatigue and diverse environmental factors, and to develop tools that might predict changes in soldier performance. Different aspects of performance were investigated, including learning a new activity, experience, and adaptability to fatigue, and gait variability was quantified through different approaches. When examining how individual learn a novel obstacle course task, we found that certain aspects of gait variability had strong associations with learning rate. In a load carriage task, variability measures were determined to be more sensitive to difference in experience level and load condition compared to typical average measures of gait. Specifically, variability increased with load, and the experienced group was less variable overall and more stable in the long term. Lastly, a subset of gait variability measures were associated with individual differences in fatigue-related changes in performance during an obstacle course. In summary, the results presented here support that it may be possible to both anticipate and enhance task performance based on individual variability. This work also provides guidelines for future research and the development of training programs specifically for improving military training, performance prediction, and performance enhancement. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:22495 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/94346 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Motor variability | en |
| dc.subject | Motor learning | en |
| dc.subject | Adaptability | en |
| dc.subject | Joint kinematics | en |
| dc.subject | Spatiotemporal variability | en |
| dc.subject | Inter-joint coordination | en |
| dc.subject | Detrended fluctuation analysis | en |
| dc.subject | Lyapunov exponents | en |
| dc.subject | Fatigue | en |
| dc.subject | Load carriage | en |
| dc.subject | Obstacle Course | en |
| dc.subject | Gait | en |
| dc.title | Gait Variability for Predicting Individual Performance in Military-Relevant Tasks | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Industrial and Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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