US2014066816A1PendingUtilityA1

Method, apparatus, and system for characterizing gait

Assignee: MCNAMES JAMESPriority: Dec 7, 2008Filed: Jun 17, 2013Published: Mar 6, 2014
Est. expiryDec 7, 2028(~2.4 yrs left)· nominal 20-yr term from priority
A61B 2560/0475A61B 5/4082A61B 5/1101A61B 5/0024A61B 2562/0219A61B 5/002A61B 5/7246A61B 5/112A61B 5/7257A61B 5/0004A61B 5/6831H04W 56/002H04L 1/08
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Claims

Abstract

Disclosed embodiments relate to methods, apparatuses, and systems for characterizing gait. Specifically, disclosed embodiments are related methods, apparatuses, and systems for characterizing gait with wearable and wirelessly synchronized inertial measurement units. These include a method for gait characterization that comprises (a) detecting zero-velocity periods using two or more wearable and wirelessly synchronized movement monitoring devices including a triaxial accelerometer and a triaxial gyroscope and (b) calculating temporal measures of gait during walking by estimating the change in position and orientation during each step.

Claims

exact text as granted — not AI-modified
What is claimed is: 
     
         1 . A method for gait characterization comprising:
 (a) detecting zero-velocity periods using two or more wearable and wirelessly synchronized movement monitoring devices, said movement monitoring devices comprising a triaxial accelerometer and a triaxial gyroscope with a bandwidth of at least 15 Hz; and   (b) calculating temporal measures of gait during walking by estimating the change in position and orientation during each step.   
     
     
         2 . The method of  claim 1 , wherein said calculating temporal measures of gait includes performing template matching based on the magnitude of said accelerometer's and said gyroscope's signals from both feet. 
     
     
         3 . The method of  claim 2 , wherein said template matching is characterized by 1) enabling multiple iterations to refine a final template, 2) weighting each template by the standard deviation of said template across detected steps, 3) scaling the template's error to be equal to one when said movement monitoring devices are stationary, 5) using a fast method based on a fast Fourier transform configured for calculating the template's matching error, or combinations thereof. 
     
     
         4 . The method of  claim 3 , wherein said calculating temporal measures of gait further comprises: a) detecting initial steps, b) estimating a gait cycle duration, c) building an initial template, d) calculating a template match, e) detecting initial template steps, f) validating steps, g) adding missed steps, or combinations thereof. 
     
     
         5 . The method of  claim 4 , wherein calculating temporal measures of gait further comprises measuring asymmetry using time-proximate steps by combining left and right steps into consecutive left-right pairs. 
     
     
         6 . The method of  claim 5 , wherein calculating temporal measures of gait further comprises characterizing gait during normal periods of walking by isolating sequences of steps in which the subject is traveling forward on a flat surface based on changes in height, bank angle, elevation angle, and heading angle. 
     
     
         7 . The method of  claim 6 , wherein calculating temporal measures of gait further comprises generating an indicator of foot drop and fall risk by characterizing the pitch of the foot with wearable sensors at the moments of heel strike and toe-off. 
     
     
         8 . The method of  claim 7 , wherein calculating temporal measures of gait further comprises characterizing the lateral deviation in a sequence of two steps resulting in three foot placements based on how far a middle foot placement deviates from a straight path from a first to a last foot placement. 
     
     
         9 . The method of  claim 8 , wherein calculating temporal measures of gait further comprises measuring the lateral swing of the foot during a single step. 
     
     
         10 . The method of  claim 9 , wherein said method comprises: 1) upsampling, 2) estimating biases, 3) calculating magnitudes, 4) finding still periods, 5) calculating positions, 6) detecting steps, 7) finding and validating step sequences, and 8) calculating gait metrics.

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