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

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 11; pp. 2278 - 2285
• Main Authors: Huang, Yangjian, Jirattigalachote, Wisit, Cutkosky, Mark R., Zhu, Xiangyang, Shull, Peter B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Adolescent; Adult; Algorithms; Complementary filter; Computation; Estimation; Female; Foot; Foot - physiology; Gait; Gyroscopes; Humans; Laboratories; Legged locomotion; Magnetometers; Male; Monitoring, Ambulatory - instrumentation; Motion perception; position and orientation tracking; Progressions; Sensors; Shoes; Signal Processing, Computer-Assisted - instrumentation; Trajectory; Walking; Wearable; Young Adult; zero-velocity update
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2016.2523512

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° 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.