The human body has substantial kinetic and kinematic degrees-of-freedoms, so redundant solutions are available for the central nervous system (CNS) to perform a repetitive task. Due to these redundancies, inherent variations exist in human movement, called motor variability (MV). Current evidence suggests that MV can be beneficial, and that there is an inverse association between MV and risk of injury. To better understand how the CNS manipulates MV to reduce injury risks, we investigated the effects of individual differences, task-relevant aspects, and psychological factors as modifiers of MV. Earlier work found that experienced workers adapted more stable movements than novices in repetitive lifting tasks. To expand on this, we quantified how MV differs between experienced workers and novices in different lifting conditions (i.e., lifting asymmetry and fatigue). Three different measures (cycle-to-cycle SD, sample entropy, and the goal equivalent manifold) were used to quantify MV. In a symmetric lifting task, experienced workers had more constrained movement than novices, and experienced workers exhibited more consistent behavior in the asymmetric condition. Novices constrained their movements, and could not maintain the same level of variability in the asymmetric condition. We concluded that experienced workers adapt stable or flexible strategies depending on task difficulty. In a prolonged lifting task, both groups increased their MV to adapt to fatigue; they particularly increased variability in a direction that had no effects on their main task goal. Developing fatigue also makes it difficult for individuals maintain the main goal. Based on these results, we conclude that increasing variability is an adaptive strategy in response to fatigue. We also assessed variability in gait parameters to compare gait adaptability using a head-worn display (HWD) compared with head-down displays for visual information presentation. An effective strategy we observed for performing a cognitive task successfully during walking was to increase gait variability in the goal direction. In addition, we found that head-up walking had smaller effects on MV, suggesting that HWDs are a promising technology to reduce adverse events during gait (e.g., falls). In summary, these results suggest that MV can be a useful indicator for evaluating some occupational injury risks. / Ph. D. / Whenever an individual performs a repetitive task, we can observe variations in their movement patterns. The magnitude of these variations, which are called motor variability, may be related to the risk of injury. To better understand this relationships, we investigated how different risk factors affect the patterns of human movement. In two studies, we compared movement patterns of experienced workers and novices in a repetitive lifting task. In a simple, brief lifting task, novices had more variations in their movement patterns. However, novices did not have the same level of variation in asymmetric lifting tasks, and constrained their movement more than experienced workers. Experienced workers, though, had a similar level of variation in both simple and more difficult lifting conditions. We concluded that whether stable or flexible movement pattern are used depends on task difficulty and the level of experience. In a longer-duration lifting task, both experienced workers and novices increased variations in their movement patterns over time, and we believe that these increases were an adaptation to fatigue. In a third study, we investigated the differences between variations in walking pattern when people use different types of information display (i.e., paper, cellphone, and smart glasses). Using smart glasses had a smaller effect on movement patterns, suggesting that this technology is potentially is safer than other types of display. In summary, these results suggest that studying the variations in human movement patterns can be a useful indicator to evaluate the risk of injury.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/77947 |
| Date | 07 June 2017 |
| Creators | Sedighi, Alireza |
| Contributors | Industrial and Systems Engineering, Nussbaum, Maury A., Kong, Zhenyu, Srinivasan, Divya, Ross, Shane D. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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