Evidence map›Paper›PMID 38400466›Full record

ArticleSensors (Basel, Switzerland)2024

Engineering Features from Raw Sensor Data to Analyse Player Movements during Competition.

Valerio Antonini, Alessandra Mileo, Mark Roantree

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Valerio AntoniniSchool of Computing, Dublin City University, Dublin 9, D09 V209 Dublin, Ireland.ORCID 0009-0004-2607-9623
Alessandra MileoSchool of Computing, Dublin City University, Dublin 9, D09 V209 Dublin, Ireland.
Mark RoantreeSchool of Computing, Dublin City University, Dublin 9, D09 V209 Dublin, Ireland.ORCID 0000-0002-1329-2570

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, and longitudes. While the data capture is simple and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions. The main goal of this research is to develop a multistep feature engineering framework that delivers the transformation of sequential data into feature sets more suited to machine learning applications.

Indexed as

RunningWearable Electronic DevicesMachine LearningMovementTeam Sportsfeature engineeringmachine learningwearable devices

Identifiers

PMID38400466
PMCPMC10893073

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.