ArticleIEEE sensors journal2024
mmPose-FK: A Forward Kinematics Approach to Dynamic Skeletal Pose Estimation Using mmWave Radars.
Article in IEEE sensors journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Gait Analysis Using mmWave Radar: A Skeleton-Based Approach with Point Cloud Transformer.IEEE transactions on medical robotics and bionics · 2026Article
- Recognition of Soccer Player Actions Using a Synchronized Multi-Camera and mm-Wave Radar Platform.Sensors (Basel, Switzerland) · 2026Article
- mmWave Radar for Sit-to-Stand Analysis: A Comparative Study With Wearables and Kinect.IEEE transactions on bio-medical engineering · 2025Article
- A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar.Sensors (Basel, Switzerland) · 2025Article
- A Sliding Window-Based CNN-BiGRU Approach for Human Skeletal Pose Estimation Using mmWave Radar.Sensors (Basel, Switzerland) · 2025Article
- Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment.IEEE open journal of engineering in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In this paper, we propose mmPose-FK, a novel millimeter wave (mmWave) radar-based pose estimation method that employs a dynamic forward kinematics (FK) approach to address the challenges posed by low resolution, specularity, and noise artifacts commonly associated with mmWave radars. These issues often result in unstable joint poses that vibrate over time, reducing the effectiveness of traditional pose estimation techniques. To overcome these limitations, we integrate the FK mechanism into the deep learning model and develop an end-to-end solution driven by data. Our comprehensive experiments using various matrices and benchmarks highlight the superior performance of mmPose-FK, especially when compared to our previous research methods. The proposed method provides more accurate pose estimation and ensures increased stability and consistency, which underscores the continuous improvement of our methodology, showcasing superior capabilities over its antecedents. Moreover, the model can output joint rotations and human bone lengths, which could be further utilized for various applications such as gait parameter analysis and height estimation. This makes mmPose-FK a highly promising solution for a wide range of applications in the field of human pose estimation and beyond.
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Registered trials
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