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Abstract
En 中文
Anteflexion angle is an essential health indicator to assess physical flexibility, and image recognition has become an effective method to detect physical anteflexion angle. In this paper, we present a physical flexibility detection algorithm under complex backgrounds, based on edge detection with transformers (ED-Former). The algorithm adopts the lightweight self-attention mechanism from the transformer architecture and combines it with an edge detection network. Additionally, we design the angle protector, which is added to the output to reduce the effect of weak edges in the problem image. Then, based on the obtained edge information, we propose a low-complexity ergonomics-based physical regions combination strategy to calculate the anteflexion angle. We select the regions of the shoulder, waist, and knee, and calculate the angle based on the relation between these regions. The experimental results show that the ED-Former has an excellent performance in physical edge detection tasks-notably producing an F1 score of 0.833 and outperforming state-of-the-art CNNs and transformers on these tasks. Furthermore, our network is effective in preventing adversarial attacks. Overall, we find that the average error of anteflexion angle with respect to the true is 1.267 degrees, well within the 5 degrees requirement for physical flexibility detection.
Keywords:
Physical flexibility
Complex backgrounds
ED-Former
Physical regions combination
Journal
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