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

16 Scopus citations


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
Article number2786
JournalSensors (Switzerland)
Issue number7
StatePublished - 1 Apr 2022


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


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