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Meta-Transfer-Learning-Based Multimodal Human Pose Estimation for Lower Limbs.

Guoming Du¹, Haiqi Zhu², Zhen Ding³

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This summary is machine-generated.

This study introduces a meta-transfer learning framework for accurate human pose estimation (HPE). It efficiently adapts models to new individuals using few-shot learning and multimodal data, reducing data requirements for personalized motion analysis.

Keywords:

human pose estimation knowledge fusion meta learning multimodal sEMG transfer learning

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Area of Science:

Robotics and Human-Computer Interaction
Biomedical Engineering
Machine Learning

Background:

Accurate human pose estimation (HPE) is crucial for personalized interactive systems like cooperative robots and healthcare exoskeletons.
Current methods require extensive datasets and frequent model updates, proving resource-intensive and time-consuming for individual adaptation.

Purpose of the Study:

To develop a resource-efficient meta-transfer learning framework for accurate and stable human pose estimation (HPE).
To enable rapid adaptation of HPE models to new individuals using minimal data through few-shot learning.

Main Methods:

Integration of multimodal inputs: high-frequency surface electromyography (sEMG), visual-inertial odometry (VIO), and high-precision image data.
A knowledge fusion strategy to enhance accuracy and stability by resolving data alignment issues.
A few-shot learning approach for efficient real-time adaptation of encoders and decoders.

Main Results:

The proposed framework achieves accurate and high-frequency human pose estimations, especially for intra-subject adaptation.
Demonstrated efficient adaptation to new individuals with only a few samples.
Successfully addressed data alignment issues through knowledge fusion.

Conclusions:

The meta-transfer learning framework offers an effective solution for personalized motion analysis.
Enables efficient, data-minimal adaptation for real-time applications in human-computer interaction and healthcare.
Advances the field of human pose estimation by improving adaptability and reducing computational burden.