3D foot kinetics estimation from distributed vGRF from smart insoles via 1D domain transformation

Understanding foot kinetics is fundamental to analyzing human locomotion, offering critical insights into mechanical loads exerted on the feet. While vertical ground reaction force (vGRF) is widely used in biomechanics research, comprehensive 3D kinetic measurements, including ground reaction force (GRF), ground reaction moment (GRM), and center of pressure (CoP) along the anterior-posterior and medial-lateral axes, provide deeper insights for various applications. Smart insoles, though portable, cost-effective, and user-friendly, primarily capture vGRF and often generate lower-quality data than force plates and instrumented treadmills. This study leverages deep learning-based domain transformation to generate instrumented treadmill-level 3D-GRF&M-CoP from distributed vGRF signals recorded by smart insoles for healthy subjects. Additionally, a multi-segment analysis is performed to identify the most relevant plantar regions for each kinetic parameter. The proposed approach is rigorously evaluated against treadmill data and benchmarked against state-of-the-art methods, accounting for subject variations and walking speeds. Key contributions include: (1) transforming distributed vGRF into 3D-GRF&M-CoP using 1D sequence-to-sequence (1D-s2s) models, (2) enhancing insole vGRF to treadmill quality, (3) optimizing insole pressure sensor layout for efficient 3D kinetics estimation, and (4) introducing Ke2KeNet, a novel deep learning model that outperforms current 1D-s2s benchmarks.