We present a novel local-global feature fusion framework for body-weight
exercise recognition with floor-based dynamic pressure maps. One step further
from the existing studies using deep neural networks mainly focusing on global
feature extraction, the proposed framework aims to combine local and global
features using image processing techniques and the YOLO object detection to
localize pressure profiles from different body parts and consider physical
constraints. The proposed local feature extraction method generates two sets of
high-level local features consisting of cropped pressure mapping and numerical
features such as angular orientation, location on the mat, and pressure area.
In addition, we adopt a knowledge distillation for regularization to preserve
the knowledge of the global feature extraction and improve the performance of
the exercise recognition. Our experimental results demonstrate a notable 11
percent improvement in F1 score for exercise recognition while preserving
label-specific features.

我们提出了一种新颖的本地 - 全局特征融合框架，用于基于地面动态压力图的体重训练识别。通过使用图像处理技术和 YOLO 目标检测来结合本地和全局特征，我们的方法在生成高级本地特征的同时考虑了不同身体部位的压力分布和物理约束，同时通过知识蒸馏来提高训练性能。实验证明，我们的方法在体重训练识别方面取得了显著的 11% 提升，并保持了特定标签特征的准确性。