Temporal changes in motor variability during prolonged lifting/lowering and the influence of work experience

Sedighi, Alireza; Nussbaum, Maury A.

doi:10.1016/j.jelekin.2017.09.001

Cited by 16 publications

(12 citation statements)

References 64 publications

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“…As such, BOX parameters may not reflect differences due to task asymmetry in terms of relative variability or regularity. This finding is consistent with findings of our earlier study of a prolonged lifting task 36 . In addition, cycle-to-cycle SD, a linear class of methods, captured differences between task asymmetry conditions for the BOX, but not for the COM.…”

Section: Discussionsupporting

confidence: 94%

“…Raw data were low-pass filtered (bi-directional, 2 nd -order Butterworth) with a cut-off frequency of 5 Hz. The initiation of each lifting cycle (lifting/lowering) was defined at the time when BOX velocity exceeded 3% of its peak value in that cycle subsequent to the time that the BOX was at rest on a shelf 36,37 . Data from first and last cycles were excluded from further analysis.…”

Section: Methodsmentioning

confidence: 99%

“…It was also of interest whether the CNS might employ different strategies to control the end effector vs. body movement. MV of the whole-body center-of-mass (COM) was used to quantify the latter, since earlier results suggested that the CNS controls the COM to regulate movement 4,36 .…”

Section: Methodsmentioning

confidence: 99%

“…In the study from which the current data were obtained 35 , participants were required to maintain pacing of the lifting task; therefore, a constant time was considered as the main goal in our GEM analysis. In an earlier report 36 , we presented a time-GEM strategy method 39 for a lifting task (see appendix B) and employed the same method here. As illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks

Sedighi

Nussbaum

2019

Sci Rep

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The substantial kinematic degrees-of-freedom available in human movement lead to inherent variations in a repetitive movement, or motor variability (MV). Growing evidence suggests that characterizing MV permits a better understanding of potential injury mechanisms. Several diverse methods, though, have been used to quantify MV, but limited evidence exists regarding the merits of these methods in the occupational context. In this work, we explored different classes of methods for characterizing MV during symmetric and asymmetric box lifting tasks. Kinematic MV of both the whole-body center-of-mass (COM) and the box were quantified, using metrics derived from a linear method (Standard Deviation), a non-linear method (Sample Entropy; an index of movement regularity), and a novel application of an equifinality method (Goal Equivalent Manifold; an index related to the set of effective motor solutions). Our results suggest that individuals manipulate regularity and the set of effective motor solutions to overcome unwanted motor noises related to the COM. These results, together with earlier evidence, imply that individuals may prioritize stability over variability with increasing task difficulty. Task performance also appeared to deteriorate with decreasing variability and regularity of the COM. We conclude that diverse metrics of MV may be complimentary to reveal differences in MV.

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Section: Discussionsupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks

Sedighi

Nussbaum

2019

Sci Rep

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“…Among the methods in the noted three classes, the GEM method is the only approach that can be employed to simultaneously quantify both variability in magnitude and the temporal structure of variations [ 38 ]. The GEM method has recently been implemented successfully in studies of diverse activities such as lifting [ 39 ], trunk flexion/extension [ 40 ], reaching [ 41 ], and walking [ 18 , 42 – 44 ].…”

Section: Introductionmentioning

confidence: 99%

Information presentation through a head-worn display (“smart glasses”) has a smaller influence on the temporal structure of gait variability during dual-task gait compared to handheld displays (paper-based system and smartphone)

Sedighi

Ulman

2018

PLoS ONE

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The need to complete multiple tasks concurrently is a common occurrence both daily life and in occupational activities, which can often include simultaneous cognitive and physical demands. As one example, there is increasing availability of head-worn display technologies that can be employed when a user is mobile (e.g., while walking). This new method of information presentation may, however, introduce risks of adverse outcomes such as a decrement to gait performance. The goal of this study was thus to quantify the effects of a head-worn display (i.e., smart glasses) on motor variability during gait and to compare these effects with those of other common information displays (i.e., smartphone and paper-based system). Twenty participants completed four walking conditions, as a single task and in three dual-task conditions (three information displays). In the dual-task conditions, the information display was used to present several cognitive tasks. Three different measures were used to quantify variability in gait parameters for each walking condition (using the cycle-to-cycle standard deviation, sample entropy, and the “goal-equivalent manifold” approach). Our results indicated that participants used less adaptable gait strategies in dual-task walking using the paper-based system and smartphone conditions compared with single-task walking. Gait performance, however, was less affected during dual-task walking with the smart glasses. We conclude that the risk of an adverse gait event (e.g., a fall) in head-down walking conditions (i.e., the paper-based system and smartphone conditions) were higher than in single-task walking, and that head-worn displays might help reduce the risk of such events during dual-task gait conditions.

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A novel method to calculate the anomaly score of movement variability of repetitive tasks

Hiranai

Kuramoto

Seo

2020

Hum Ftrs & Erg Mfg Svc

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This study proposes a new calculation method for the anomaly score of repetitive tasks based on singular spectrum transformation (SST) that accounted for a longterm history of human motion. To validate the efficacy of the proposed method, the calculated anomaly score was compared with movement variability computed by a traditional method and to its SST-computed score. Eleven male participants performed repetitive lightweight material handling tasks under different work conditions and an electromagnetic tracking system measured their working posture. Movement variability and anomaly score on the shoulder and elbow joints were calculated based on measured working postures. The movement variability on the elbow flexion angle increased with time. In contrast, the anomaly score of the elbow flexion angle decreased with time, but shoulder flexion and inner rotation angles showed increased scores with the passage of time. These findings are similar to those of previous studies that stated that movement variability increased from redundant degrees of freedom available for performing multi-joint movements; this occurred due to the development of muscle fatigue on the shoulder joint from performing repetitive tasks. On comparing this to the anomaly scores calculated by conventional SST, it was observed that the score computed by the proposed method reflected the whole trend of human motion in repetitive tasks and did not depend on local problems in working posture. Therefore, it was concluded that the new method of calculating the anomaly score is more suitable to detect changes in movement variability in repetitive tasks.

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Temporal changes in motor variability during prolonged lifting/lowering and the influence of work experience

Cited by 16 publications

References 64 publications

Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks

Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks

Information presentation through a head-worn display (“smart glasses”) has a smaller influence on the temporal structure of gait variability during dual-task gait compared to handheld displays (paper-based system and smartphone)

A novel method to calculate the anomaly score of movement variability of repetitive tasks

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