Development of Foot Mat Sensor Technology for Foot Identification and BMI-Based Biomechanical Risk Prediction - Evanita; Slamet Khoeron; Andre Tri Saputra; Curie Habiba (Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta, 2025)
This study advances the Foot Mat Sensor (FMS) technology to discern foot morphology and forecast biomechanical vulnerabilities predicated on Body Mass Index (BMI). The proposed system amalgamates the analysis of plantar pressure with various biomechanical parameters, including heel pressure, midfoot pressure, forefoot pressure, and foot contact area (FCA). Data were collected from ten participants exhibiting a spectrum of BMI, foot morphology (High Arch, Normal Arch, and Low Arch), foot length, contact area, and asymmetrical plantar pressure. The findings indicated a statistically significant correlation between elevated BMI (>25), irregular plantar pressure distribution, and heightened biomechanical risk. Participants with high BMI and Low Arch (LA) foot morphology demonstrated an augmented risk, with plantar pressure asymmetry ≥20 kPa as the principal indicator. The prediction model founded on the Random Forest algorithm attained an accuracy of 85% in categorizing biomechanical risk into low, medium, and high classifications. The Digital Footprint Scanner technology, innovated through this research, is anticipated to augment the efficacy of personalized and precise diagnostics and the prophylaxis of biomechanical injuries. This endeavor contributes to formulating a data-driven system for the early detection of biomechanical risks, with applications in medicine, athletics, and rehabilitation.