Novel Foot Progression Angle Algorithm Estimation via Foot-Worn, Magneto-Inertial Sensing

Objective: The foot progression angle (FPA) is an important clinical measurement but currently can only be computed while walking in a laboratory with a marker-based motion capture system. This paper proposes a novel FPA estimation algorithm based on a single integrated sensor unit, consisting of an accelerometer, gyroscope, and magnetometer, worn on the foot. Methods: The algorithm introduces a real-time heading vector with a complementary filter and utilizes a gradient descent method and zero-velocity update correction. Validation testing was performed by comparing FPA estimation from the wearable sensor with the standard FPAs computed from a marker-based motion capture system. Subjects performed nine walking trials of 2.5 min each on a treadmill. During each trial, subjects walked at one speed out of three options (1.0, 1.2, and 1.4 m/s) and walked with one gait pattern out of three options (normal, toe-in, and toe-out). Results: The algorithm estimated FPA to within 0.2 $^{\circ }$ of error or less for each walking conditions. Conclusion: A novel FPA algorithm has been introduced and described based on a single foot-worn sensor unit, and validation testing showed that FPA estimation was accurate for different walking speeds and foot angles. Significance: This study enables future wearable systems gait research to assess or train walking patterns outside a laboratory setting in natural walking environments.