# IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose

arXiv: https://arxiv.org/abs/2411.12676 · HTML: https://mlsanity.com/paper/2411.12676/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 101 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset](https://mlsanity.com/paper/2406.13113/) (2024)
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- [Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning](https://mlsanity.com/paper/2408.16068/) (2024)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion](https://mlsanity.com/paper/2408.07303/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [The Design of Autonomous UAV Prototypes for Inspecting Tunnel Construction Environment](https://mlsanity.com/paper/2408.07286/) (2024)
- [Few-shot Name Entity Recognition on StackOverflow](https://mlsanity.com/paper/2404.09405/) (2024)
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- [Mix of Experts Language Model for Named Entity Recognition](https://mlsanity.com/paper/2404.19192/) (2024)
- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Graphical Structural Learning of rs-fMRI data in Heavy Smokers](https://mlsanity.com/paper/2409.08395/) (2024)
- [LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting](https://mlsanity.com/paper/2410.15286/) (2024)
- [Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm](https://mlsanity.com/paper/2411.06720/) (2024)
- [TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model](https://mlsanity.com/paper/2410.15283/) (2024)

## Built on by (most cited first, 7 of 7)

- [Optimized CNNs for Rapid 3D Point Cloud Object Recognition](https://mlsanity.com/paper/2412.02855/) (2024)
- [CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning](https://mlsanity.com/paper/2503.10045/) (2025)
- [MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic](https://mlsanity.com/paper/2507.00304/) (2025)
- [Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery](https://mlsanity.com/paper/2604.11961/) (2026)
- [Construction and optimization of health behavior prediction model for the elderly in smart elderly care](https://mlsanity.com/paper/2412.02062/) (2024)
- [Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning](https://mlsanity.com/paper/2509.12269/) (2025)
- [DT-NeRF: A Diffusion and Transformer-Based Optimization Approach for Neural Radiance Fields in 3D Reconstruction](https://mlsanity.com/paper/2509.17232/) (2025)

## Abstract (arXiv)

This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extraction, OpenPose for real-time keypoint detection, and Bayesian optimization for hyperparameter tuning. Experimental results on NTURGB+D and FineGYM datasets demonstrate superior performance, with AP(p50) scores of 90.5 and 91.0, and mAP scores of 74.3 and 74.0, respectively. Ablation studies confirm the essential roles of each module in enhancing model accuracy. IE-PONet provides a robust tool for athletic performance analysis and optimization, offering precise technical insights for training and injury prevention. Future work will focus on further model optimization, multimodal data integration, and developing real-time feedback mechanisms to enhance practical applications.