GAIT Toolbox (DOI: https://zenodo.org/record/8187922) The GAIT toolbox is a collection of MATLAB routines to estimate gait measures from raw acceleration data. Many people (and articles) have contributed to the GAIT Toolbox; at the bottom of this file you will find an overview. INSTRUCTIONS Include the "GAIT Toolbox" folder incl. its sub folders to your MATLAB path ("Set Path" can be found in MATLAB's ENVIRONMENT tool menu). If you plan to use gaitAnalysis.m (see below), also add the root folder (the one in which gaitAnalysis.m and the "GAIT Toolbox" folder reside, defaultly named "Gait-Analysis") to the MATLAB-path (without subfolders). If the path is set, you can run gaitAnalysis.m (graphical tool, see PDF-manual in the doc folder), or process a single input file from the command line through gaitAnalyse.m (see below). gaitAnalyseMany.m is a simple example how to run gaitAnalyse.m mulitiple times with different input files (in the graphical tool you can create a 'batch" for this). CONTENTS Folder "GAIT Toolbox/Frondend utilities" contains utilities used by gaitAnalyse to parse input files, read the measurement data (using McRoberts code), and calculate and present the gait estimates (using the algorithms in the below two folders). Folder "GAIT Toolbox/Gait Quality Estimates/" contains most of the algorithms. Folder "GAIT Toolbox/Get Aggregate Values/" contains utilities to collect the calculated values. Folder "GAIT Toolbox/Output Utilities/" contains some utilities for the main function gaitAnalyse() to save the results in different output formats. Folder "GAIT Toolbox/GUI utilities" contains utilities used by the graphical tool as well as some utilities to redirect console output to the grahical tool (warning: editing these files can easily break the graphical tool). Folder "GAIT Toolbox/OMXreadfiles/" contains the McRobert's utilities to read the raw measurment files. Folder "exe" contains files to install executable versions of gaitAnalysis (graphical tool) and gaitAnalyse (console program; run gaitAnalyse -help from the command prompt to see how to use). They will download and install the appropriate MATLAB runtime system if needed. Main function function gaitAnalyse (parametersFile, [OPTIONS]) % Process 'parameterFile' and subsequently: % - load locomotion episodes % - calculate measures % - collect aggregated values % - write results % % INPUT % % parametersFile: Path to parameter file containg all the gait parameters % % OPTIONS % % 'verbosityLevel' 0 minimal output % 1 normal output (default) % 2 include debug info % % 'saveToJSON' true in addtion to MATLAB- and textformat, write % results in JSON-format % false do not write JSON files (default) % % 'saveToSPSS' true in addtion to MATLAB- and textformat, write % results in SPSS-format % false do not write SPSS files (default % % 'overwriteFiles' 0 never overwrite existing files (default) % 1 overwrite aggregated values, but do use saved % episodes and measures if possible % 2 overwrite measures and aggregated % values, but do use saved episodes if possible % 3 recalcuate everything % % 'overwriteEpisodes' true == 'overwriteFiles', 3 % 'overwriteMeasures' true == 'overwriteFiles', 2 % 'overwriteAggregatedValues' true == 'overwriteFiles', 1 % % Note: - default value for above three options is false, mimicking % 'overwriteFiles', 0 % - if 'overwriteFiles' is specified, above three options are % ignored % - saveToJSON and saveToSPSS, if specified, overrule the % settings in paramatersFile EXAMPLES If called with no input parameters, a popup will appear to choose a parameter file (or individual input files if no parameter file is chosen): gaitAnalyse To process 'DATA/GaitParams.txt' and recalculate everything, use either: gaitAnalyse("DATA/GaitParams.txt", 'overwriteFiles', 3) or gaitAnalyse("DATA/GaitParams.txt", 'overwriteEpisodes', true); If valid episodes have already been extracted, use either: gaitAnalyse("DATA/GaitParams.txt", 'overwriteFiles', 2) or gaitAnalyse("DATA/GaitParams.txt", 'overwriteMeasures', true) If calculated measures are still valid, but aggregated values need to be recalcuted (e.g., because the percentiles as specified in the parameter file have been changed), use either: gaitAnalyse("DATA/GaitParams.txt", 'overwriteFiles', 1) or gaitAnalyse("DATA/GaitParams.txt", 'overwriteAggregatedValues', true) If less output is desired, use: gaitAnalyse("DATA/GaitParams.txt", 'verbosityLevel', 0) To suppress writing JSON files, use: gaitAnalyse("DATA/GaitParams.txt", 'saveToJSON', false) (or specify "Save to SPSS = no" in the parameter file; see below) EXAMPLE OF A PARAMETER FILE % Parameter settings: Classification file = ?.csv % mandatory Raw measurement file = ?.OMX % mandatory Leg length** = 0.925 % mandatory Epoch length*** = 10 % defaults to 10 Cutoff frequency** = 0.5 % defaults to 0.5 (used in Butterworth % filter to counteract integration drift) Hours to skip at start of measurement* = 6 % defaults to 0; hours may be % replaced by seconds or minutes Percentiles* = [10 50 90] % defaults to [20 50 80] % ***changing requires recalculation of locomotion episodes, locomotion % measures, and aggregated values % **changing requires recalculation of locomotion measures and aggregated % values % *changing requires recalculation of aggregated values % To include physical activity information from the classification file: Get physical activity from classification = yes Minimum sensor wear time per day = 18 Minimum number of valid days for activities = 2 Minimum number of valid days for lying = 3 % Measures that can be requested through the parameter file: Walking Speed = yes Stride Length = yes Stride Regularity* = yes Sample Entropy* = yes RMS* = yes Index Harmonicity* = yes Power At Step Freq* = yes Gait Quality Composite Score = yes Bimodal Fit Walking Speed = yes % does not always converge to the exact % same solution Preferred Walking Speed = 0.96 % corresponding percentile will be % reported % *Add VT, ML, or AP to request individual directions, e.g., RMS VT. % Output format of the above measures (in addition to text and MATLAB) Save to JSON = yes % default = yes Save to SPSS = yes % default = no REFERENCES Several articles are referred to in the MATLAB codes. Below you will find a full reference, including a web link. 1) A. Wolf, J.B. Swift, H.L. Swinney, and J.A. Vastano (1985). Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena 16(3): 285-317. https://doi.org/10.1016/0167-2789(85)90011-9 2) J.S. Richman, and J.R. Moorman (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology - Heart and Circulatory Physiology, 278(6): H2039-2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039 3) W. Zijlstra & L. Hof (2003). Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & Posture, 18(2): 1-10. https://doi.org/10.1016/S0966-6362(02)00190-X 4) R. Moe-Nilssen and J.L. Helbostad (2004). Estimation of gait cycle characteristics by trunk accelerometry. Journal of Biomechanics, 37(1): 121-126. https://doi.org/10.1016/S0021-9290(03)00233-1 5) D. Kugiumtzis and A. Tsimpiris (2010). Measures of analysis of time series (MATS): a Matlab toolkit for computation of multiple measures on time series data bases. Journal of Statistical Software, 33(5); 1-30. https://www.jstatsoft.org/article/view/v033i05 6) A. Weiss, M. Brozgol, M. Dorfman, T. Herman, S. Shema, N. Giladi, and J.M. Hausdorff (2013). Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabilitation and Neural Repair, 27(8): 742-752. https://doi.org/10.1177/1545968313491004 7) S.M. Rispens, K.S. van Schooten, M. Pijnappels, A. Daffertshofer, P.J. Beek, and J.H. van Dieën (2014). Identification of fall risk predictors in daily life measurements: gait characteristics� reliability and association with self-reported fall history. Neurorehabilitation and Neural Repair, 29(1): 54-61. https://doi.org/10.1177/1545968314532031 8) K.S. van Schooten, M. Pijnappels, S.M. Rispens, P.J.M. Elders, P. Lips, and J.H. van Dieën (2015). Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults. Journals of Gerontology, Series A:Biological Sciences & Medical Sciences, 70(5): 608-615. https://doi.org/10.1093/gerona/glu225 CONTRIBUTORS Many people have contributed to the GAIT toolbox, including programmers, PhD students and scientific staff members. Among others, these include (in alphabetical order): Peter Beek, Sjoerd Bruijn, Richard Casius, Andreas Daffertshofer, Jaap van Dieën, Pieter van Doorn, Mirjam Pijnappels, Markus Rieger, Sietse Rispens, Kim van Schooten, Roel Weijer and Yuge Zhang.
VU-HMS/Gait-Analysis
MATLAB Toolbox to estimate gait measures from acceleration data.
MATLABNOASSERTION