Multistage fall detection framework via 3D pose sequences and TCN integration.

Area of Science:

• Computer Science
• Artificial Intelligence
• Sports Science

Background:

• Accurate and computationally efficient fall detection in sports is challenging.
• Existing methods may lack efficiency or accuracy for dynamic sports movements.

Purpose of the Study:

• To develop a novel multi-stage fall detection framework for sports activities.
• To integrate 3D human pose estimation with temporal convolutional networks for enhanced accuracy and efficiency.

Main Methods:

• Utilized 2D human pose estimation for multi-scale spatial feature extraction.
• Reconstructed 2D poses to 3D using a domain transfer architecture.
• Employed temporal convolutions on 3D pose sequences for robust fall event recognition with low computational cost.

Main Results:

• Achieved a fall detection accuracy of 99.87% on the NTU RGB+D benchmark dataset.
• Demonstrated state-of-the-art performance in fall detection for sports activities.
• The framework proved effective and computationally efficient.

Conclusions:

• The proposed multi-stage framework effectively detects falls in sports with high accuracy.
• Integration of 3D pose sequences and temporal convolutions offers a promising approach for efficient fall detection.
• This method advances the field of sports safety and injury prevention technology.