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

Abstract

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. 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). The algorithm estimated FPA to within 0.2 ^° of error or less for each walking conditions. 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. This study enables future wearable systems gait research to assess or train walking patterns outside a laboratory setting in natural walking environments.