Evidence map›Paper›PMID 31170214›Full record

ArticlePloS one2019

Pose estimates from online videos show that side-by-side walkers synchronize movement under naturalistic conditions.

Claire Chambers, Gaiqing Kong, Kunlin Wei, Konrad Kording

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Spontaneous Interpersonal Synchronization of Gait: A Systematic Review.Archives of rehabilitation research and clinical translation · 2021
    Review
  15. Article
  16. Computer Vision to Automatically Assess Infant Neuromotor Risk.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2020
    Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Claire ChambersDepartment of Bioengineering and Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.ORCID 0000-0003-1777-0100
Gaiqing KongDepartment of Bioengineering and Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Kunlin WeiSchool of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Konrad KordingDepartment of Bioengineering and Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Funding

The role of uncertainty in human motor learning and adaptationR01NS063399 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI KORDING, KONRAD P. · 2009 to 2019
$2.8M
NINDS NIH HHS R01 NS063399
6 · The paper itself

Abstract

Marker-less video-based pose estimation promises to allow us to do movement science on existing video databases. We revisited the old question of how people synchronize their walking using real world data. We thus applied pose estimation to 348 video segments extracted from YouTube videos of people walking in cities. As in previous, more constrained, research, we find a tendency for pairs of people to walk in phase or in anti-phase with each other. Large video databases, along with pose-estimation algorithms, promise answers to many movement questions without experimentally acquiring new data.

Indexed as

InternetMovementVideo RecordingWalkersAlgorithmsAnkleHumansPosture

Identifiers

PMID31170214
PMCPMC6553729

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.