A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

Peter Ardhianto, Raden Bagus Reinaldy Subiakto, Chih-Yang Lin, Yih-Kuen Jan, Ben-Yi Liau, Jen-Yung Tsai, Veit Babak Hamun Akbari, Chi-Wen Lung

Research output: Contribution to journalArticlepeer-review


Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10 vs. 5.86 ± 0.09 , p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10 vs. 6.07 ± 0.06 , p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10 vs. 6.75 ± 0.06 , p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

Original languageEnglish (US)
Article number2786
Issue number7
StatePublished - Apr 5 2022


  • YOLO
  • object detection
  • foot problems
  • angle parameter
  • foot clinic

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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