Evidence map›Paper›PMID 34770620›Full record

ReviewSensors (Basel, Switzerland)2021

Applications of Pose Estimation in Human Health and Performance across the Lifespan.

Jan Stenum, Kendra M Cherry-Allen, Connor O Pyles, Rachel D Reetzke, Michael F Vignos, Ryan T Roemmich

Registry-linked trialAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06629038 (A Novel Technique for Estimating Maximal Jaw Movement, Neck and Shoulder Joint Range of Motion Using an Artificial Intelligence Model), which is not on this map. Cited by 61 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 1 pooled it
–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.

NCT06629038 recruitingnot on this mapstarted 2024, after this paper: background citation

A Novel Technique for Estimating Maximal Jaw Movement, Neck and Shoulder Joint Range of Motion Using an Artificial Intelligence Model

TypeobservationalSponsorNational Taiwan University HospitalRan2024 to 2026Enrolled40ConditionsOral CancerArmsobservation alone
3 · Its place in the literature

Who cites it

61 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  7. Review
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  9. Observational
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  18. Article
  19. Ataxia.Movement disorders clinical practice · 2025
    Article
  20. Article

1 more citing papers are in PubMed but not listed here.

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

6 authors.

Jan StenumCenter for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA.
Kendra M Cherry-AllenDepartment of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Connor O PylesJohns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA.ORCID 0000-0003-4712-5700
Rachel D ReetzkeCenter for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD 21211, USA.
Michael F VignosJohns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA.
Ryan T RoemmichCenter for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA.ORCID 0000-0003-0797-6455

Funding

Research Training in Rehabilitation for Brain Injury and Neurological DisabilityT32HD007414 · NICHD · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI Amy J. Bastian · 1991 to 2026
$6.3M
Leveraging energetics to improve gait rehabilitation after strokeR21AG059184 · NIA · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI ROEMMICH, RYAN THOMAS · 2018 to 2019
$445k
NIA NIH HHS R21 AG059184NICHD NIH HHS T32 HD007414
6 · The paper itself

Abstract

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

Indexed as

LongevityMovementAlgorithmsBiomechanical PhenomenaHumansMotionartificial intelligenceassessmentcomputer visiondevelopmentkinematicsmachine learningmarkerless motion capturemovement trackingpose estimation

Identifiers

PMID34770620
PMCPMC8588262

What OpenQuestion holds

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Registered trials

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.