ArticleSensors (Basel, Switzerland)2022
A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 28 citations in OpenAlex.
- Autoencoder-Enhanced Convolutional Neural Networks for Plantar Pressure-Based Gait Pattern Recognition: Model Development and Cross-Validated Evaluation Study.JMIR formative research · 2026Article
- Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Artificial intelligence in foot and ankle surgery: current concepts.Orthopadie (Heidelberg, Germany) · 2023Review
- Towards toxic and narcotic medication detection with rotated object detectors.Quantitative imaging in medicine and surgery · 2023Article
- Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review.Foot & ankle orthopaedics · 2023Article
- Decision Tree-Based Foot Orthosis Prescription for Patients with Pes Planus.International journal of environmental research and public health · 2022Article
- Deep Learning-Based System for Preoperative Safety Management in Cataract Surgery.Journal of clinical medicine · 2022Article
- Machine-learned-based prediction of lower extremity overuse injuries using pressure plates.Frontiers in bioengineering and biotechnology · 2022Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
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.
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