A Novel Foot Progression Angle Detection Method

Foot Progression Angle (FPA) detection is an important measurement in clinical gait analysis. Currently, the FPA can only be computed, while walking in a laboratory with a marker-based or Initial Measure Unit (IMU) based motion capture systems. A novel Visual Feature Matching (VFM) method is presented here, measuring the FPA by comparing the shoe orientation with the progression, i.e. the walking direction. Both the foot orientation and progression direction are detected by image processing methods in rectified digital images. Differential FPA (DFPA) algorithm is developed to provide accurate FPA measurement. The hardware of this system combines only one wearable sensor, a chest or torso mounted smart phone camera, and a laptop on the same Wi-Fi network. There is no other prerequisite hardware installation or other specialized set up. This method is a solution for long-term gait self-monitoring in a home or community like environments. Our novel approach leads to simple and persistent, real time remote gait FPA monitoring, and it is a core of new bio-feedback medical procedure.

[1]  Christopher Ferrigno,et al.  The Feasibility of Using Augmented Auditory Feedback From a Pressure Detecting Insole to Reduce the Knee Adduction Moment: A Proof of Concept Study. , 2016, Journal of biomechanical engineering.

[2]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[3]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision: From Images to Geometric Models , 2003 .

[4]  Kamiar Aminian,et al.  Gait and Foot Clearance Parameters Obtained Using Shoe-Worn Inertial Sensors in a Large-Population Sample of Older Adults , 2013, Sensors.

[5]  T V Wrigley,et al.  Altering foot progression angle in people with medial knee osteoarthritis: the effects of varying toe-in and toe-out angles are mediated by pain and malalignment. , 2013, Osteoarthritis and cartilage.

[6]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[7]  Majid Sarrafzadeh,et al.  Smart insole: a wearable system for gait analysis , 2012, PETRA '12.

[8]  Michael A Hunt,et al.  Gait modification strategies for altering medial knee joint load: A systematic review , 2011, Arthritis care & research.

[9]  Dan Dolan,et al.  Object tracking in noisy radar data: Comparison of Hough transform and RANSAC , 2013, IEEE International Conference on Electro-Information Technology , EIT 2013.

[10]  Andrew Zisserman,et al.  Metric rectification for perspective images of planes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[11]  W. Faig.  CALIBRATION OF CLOSE-RANGE PHOTOGRAMMETRIC SYSTEMS: MATHEMATICAL FORMULATION , 1975 .

[12]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  M. Hunt,et al.  Exercise, Gait Retraining, Footwear and Insoles for Knee Osteoarthritis , 2013, Current Physical Medicine and Rehabilitation Reports.

[14]  M. Cutkosky,et al.  Toe-in gait reduces the first peak knee adduction moment in patients with medial compartment knee osteoarthritis. , 2013, Journal of biomechanics.

[15]  S. Govindarajulu,et al.  A Comparison of SIFT, PCA-SIFT and SURF , 2012 .

[16]  Mark R. Cutkosky,et al.  Novel Foot Progression Angle Algorithm Estimation via Foot-Worn, Magneto-Inertial Sensing , 2016, IEEE Transactions on Biomedical Engineering.

[17]  Christian B. Redd,et al.  A wireless sensory feedback system for real-time gait modification , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Andrew J. Davison,et al.  Extrinsics Autocalibration for Dense Planar Visual Odometry , 2015, J. Field Robotics.

[19]  Micah K. Johnson,et al.  Metric Measurements on a Plane from a Single Image , 2006 .

[20]  T. M. Owings,et al.  Step width variability, but not step length variability or step time variability, discriminates gait of healthy young and older adults during treadmill locomotion. , 2004, Journal of biomechanics.

[21]  Sanjiv Singh,et al.  Monocular Visual Odometry using a Planar Road Model to Solve Scale Ambiguity , 2011, ECMR.

[22]  Christopher Kirtley,et al.  Clinical Gait Analysis: Theory and Practice , 2006 .

[23]  F. Cicuttini,et al.  Higher dynamic medial knee load predicts greater cartilage loss over 12 months in medial knee osteoarthritis , 2011, Annals of the rheumatic diseases.

[24]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[25]  Milan Simic,et al.  Developing a Navigation System for Mobile Robots , 2015 .

[26]  Shamik Sural,et al.  Segmentation and histogram generation using the HSV color space for image retrieval , 2002, Proceedings. International Conference on Image Processing.