Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models.

Jiayun Li, Ruoyu Wei, Qiantong Xie, Changfa Wu, Yoon Hyuk Kim

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In one paragraph

Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Cross-Species Behavioral Representation Learning Using Domain-Adversarial Adaptation on Wearable IMU Signals.Biomimetics (Basel, Switzerland) · 2026
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5 · Who and what money

Authors and funding

5 authors.

Jiayun LiSchool of Social Sports, Tianjin University of Sport, Tianjin 301617, China.

Ruoyu WeiDepartment of Mechanical Engineering, Kyung Hee University, Yongin 17104, Republic of Korea.

Qiantong XieCollege of Physical Education, Shenzhen University, Shenzhen 518060, China.

Changfa WuSchool of Sport and Training, Tianjin University of Sport, Tianjin 301617, China.

Yoon Hyuk KimDepartment of Mechanical Engineering, Kyung Hee University, Yongin 17104, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ground reaction force (GRF) is essential for maintaining dynamic stability and generating power during the golf swing. Traditional GRF assessment relies on force plates, limiting measurement to laboratory environments and restricting evaluation of natural, field-based performance. Recent work has explored wearable inertial measurement units (IMUs) and data-driven models to estimate GRF during simple locomotor tasks, yet no study has examined whether coupled lower-limb kinematics can predict three-dimensional GRF during complex, high-speed movements such as the golf swing. This study collected bilateral hip, knee, and ankle joint angles from IMUs, along with 3D GRF data, to evaluate five biomimetic deep learning (DL) architectures across seven sensor configurations. The TCN-BiGRU model achieved the highest accuracy (R

Indexed as

biomechanicsbiomimeticsdeep learninggolf swingground reaction forceinertial measurement unitswearable motion analysis

Identifiers

PMID41892082

DOI10.3390/biomimetics11030159

PMCPMC13023547

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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.