Evidence map›Paper›PMID 40648127›Full record

ArticleSensors (Basel, Switzerland)2025

Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor.

Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang, Weiya Hao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

6 authors.

Shangxiao LiResearch Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.
Jiahui PanResearch Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.
Dongmei WangCollege of Human Sport Science, Beijing Sport University, Beijing 100084, China.
Shufang YuanResearch Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.
Jin YangCollege of Human Sport Science, Shanghai University of Sport, Shanghai 200438, China.
Weiya HaoResearch Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.ORCID 0000-0002-8858-8827

Funding

National Natural Science Foundation of China 12302415the Fundamental Research Funds for the China Institute of Sport Science 24-45
6 · The paper itself

Abstract

Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. Acceleration data and vGRF values of eight rearfoot strikers and seven forefoot strikers running at 12, 14, and 16 km/h were collected by a single IMU and an instrumented treadmill. The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. The wavelet neural network model (WNN) and feed-forward neural network model (FFNN) were adapted to predict vGRF using three-axis or sagittal-axis acceleration data in the stance phase, respectively. One rearfoot striker and one forefoot striker were randomly selected as a test set, while the other participants formed training sets. After synchronization, mean absolute errors for stride time of the IMU and vGRF data were less than 11.2 ms. The coefficient of multiple correlations for vGRF measured curves and predicted curves was more than 0.97. The normalized root mean square errors (NRMSEs) between two curves were 4.6~9.2%, and R

Indexed as

Neural Networks, ComputerRunningAccelerationAdultAlgorithmsBiomechanical PhenomenaFemaleFootGaitHumansMaleYoung Adultartificial neural networkrunning-related injuriessynchronization algorithmwearable sensor

Identifiers

PMID40648127
PMCPMC12252099

What OpenQuestion holds

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Registered trials

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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.