Applications of Motor Variability for Assessing Repetitive Occupational Tasks

Abstract

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.