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Jaap H. van Dieën

Researcher at VU University Amsterdam

Publications -  482
Citations -  20482

Jaap H. van Dieën is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Trunk & Gait (human). The author has an hindex of 70, co-authored 452 publications receiving 17247 citations. Previous affiliations of Jaap H. van Dieën include University of British Columbia & University of Mannheim.

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Journal ArticleDOI

Learning to balance on one leg: motor strategy and sensory weighting.

TL;DR: The initial decrease in sway with practice was associated with upweighting of visual information, while later changes were associated with suppression of oscillations that the authors suggest are due to too high proprioceptive feedback gains.
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Center of pressure trajectories, trunk kinematics and trunk muscle activation during unstable sitting in low back pain patients.

TL;DR: Activation of the intersegmental longissimus relative to the iliocostalis muscle was lower in low-back pain patients compared to healthy individuals, which may be causal for larger thoraco-lumbar movements, and may also reflect the need for larger corrective movements to compensate balance impairments.
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Fall-related gait characteristics on the treadmill and in daily life

TL;DR: Gait characteristics revealed less stable, less symmetric, and more variable gait during daily life than on a treadmill, yet about half of the characteristics were significantly correlated between conditions.
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The effect of control strategies for an active back-support exoskeleton on spine loading and kinematics during lifting

TL;DR: While subtle differences in back load patterns were seen between the three control modes, no differences in peak compression forces were found and this may be related to limitations in the torque generating capacity of the EXO.
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Do Extreme Values of Daily-Life Gait Characteristics Provide More Information About Fall Risk Than Median Values?

TL;DR: Gait characteristics during optimal performance gait provide more information about the risk of falling than high-risk situations, however, their added value over medians in prediction is limited.