Work

Method and System for the Classification of Tibial‐Calcaneal Angle Using FSR Sensors and Convolutional Neural Networks

Year: 2025

Type: article

Abstract: ABSTRACT The tibial‐calcaneal angle (TCA) is a crucial biomechanical parameter used to assess foot alignment and classify rearfoot positions such as varus, rectus and valgus. Advances in plantar pressure sensing and deep learning (DL) have improved the accuracy of foot posture assessment, yet current methods rarely integrate plantar pressure with TCA, limiting precision. This manuscript presents an automated system for rearfoot classification based on plantar pressure mapping. The proposed method combines a 32 32 force‐sensitive resistor (FSR) sensor array with a convolutional neural network (CNN) to improve classification accuracy, reducing dependence on manual and radiographic techniques. Plantar pressure data were collected from 100 participants, and the CNN was trained to classify four alignment categories: rectus–rectus, rectus–valgus, valgus–valgus, and valgus–rectus. In this notation, the first term denotes the left foot and the second the right. Rectus indicates neutral alignment, while valgus refers to outward heel deviation. The model achieved 90.00% accuracy, showing strong generalization across categories. The valgus–rectus class exhibited the highest recall, approximately 93.75% with 97.58% specificity, while valgus–valgus reached 88.57% recall. Overall, the proposed system demonstrates high accuracy and robust performance, representing a clinically viable alternative to conventional image‐based methods.

Source: IET Image Processing

DOI: 10.1049/ipr2.70244