Evidence map›Paper›PMID 41536362›Full record

ArticleAnnotation of real-world data for artificial intelligence systems : 9th international workshop, ARDUOUS 2025, Bologna Italy, October 25-26, 2025, proceedings. International Workshop on Annotation of Real-World Data for Artificial Intell...2026

Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.

Hoan Tran, Veronika Potter, Umberto Mazzucchelli, Dinesh John, Stephen Intille

Abstract read
In one paragraph

Article in Annotation of real-world data for artificial intelligence systems : 9th international workshop, ARDUOUS 2025, Bologna Italy, October 25-26, 2025, proceedings. International Workshop on Annotation of Real-World Data for Artificial Intell..., 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
    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

5 authors.

Hoan TranNortheastern University, Boston, MA 02115, USA.ORCID 0000-0003-4888-0513
Veronika PotterNortheastern University, Boston, MA 02115, USA.ORCID 0009-0005-7256-7713
Umberto MazzucchelliNortheastern University, Boston, MA 02115, USA.ORCID 0009-0000-1071-6027
Dinesh JohnNortheastern University, Boston, MA 02115, USA.ORCID 0009-0002-7095-1023
Stephen IntilleNortheastern University, Boston, MA 02115, USA.ORCID 0000-0002-0287-2553

Funding

Accelerating the development of novel methods to measure 24-hr physical behaviorR01CA252966 · NCI · NORTHEASTERN UNIVERSITY · PI INTILLE, STEPHEN S · 2020 to 2024
$2.2M
NCI NIH HHS R01 CA252966
6 · The paper itself

Abstract

Researchers need ground-truth activity annotations to train and evaluate wearable-sensor-based activity recognition models. Often-times, researchers establish ground truth by annotating the video recorded while someone engages in activity wearing sensors. The "gold-standard" video annotation practice requires two trained annotators independently annotating the same footage with a third domain expert resolving disagreements. Such annotation is laborious, and so widely-used datasets have often been annotated using only a single annotator per video. Because the research community is moving towards collecting data of more complex behaviors from free-living people 24/7 and annotating more granular, fleeting activities, the annotation task grows even more challenging; the single-annotator approach may yield inaccuracies. We investigated a "silver-standard" approach: rather than using two independent annotation passes, a second annotator

Indexed as

AnnotationHuman Activity RecognitionTaxonomyVideo

Identifiers

PMID41536362
PMCPMC12799097

What OpenQuestion holds

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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.