Sensor Fusion Pipeline for Gait Analysis Using Insole-Embedded Inertial Measurement Units (IMUs)

Wearable gait assessment using foot-mounted inertial measurement units (IMUs) can extend mobility evaluation beyond the laboratory, but performance is limited by integration drift, magnetic disturbances, and gait event detection sensitivity during outdoor overground walking. This study presents an insole-embedded nine-axis IMU pipeline combining orientation estimation using the Versatile Quaternion Filter with zero-velocity update-aided error-state Kalman filtering and Rauch–Tung–Striebel smoothing. Twenty-one healthy adults performed slow, normal, and fast 100 m straight path walks and normal-speed 437.7 m full-lap walks on an outdoor track. Seventy-five retained trials were compared with the Movella MVN Awinda reference using task-specific absolute-agreement intraclass correlation coefficients (ICCs) and mixed-effects Bland–Altman analysis accounting for repeated observations within participants. Agreement was excellent for stride length and stride time (ICCs: 0.928–0.961 and 0.989–0.997, respectively), good to excellent for gait velocity (0.894–0.957), moderate to excellent for stance time (0.679–0.942), and moderate to good for swing time (0.544–0.790). Mean biases were +0.036 m/s for gait velocity and +1.54 cm for stride length; temporal biases ranged from $-0.005$ to $+0.005$ s. Walking distance mean absolute percentage errors ranged from 0.55% to 1.20%. These findings support the feasibility of insole-based spatiotemporal gait assessment during predefined outdoor walking in healthy adults. Further validation against independent reference systems and in clinical populations and more complex environments is needed.