Literature DB >> 16212968

Classification of gait patterns in the time-frequency domain.

M N Nyan1, F E H Tay, K H W Seah, Y Y Sitoh.   

Abstract

This paper describes the classification of gait patterns among descending stairs, ascending stairs and level walking activities using accelerometers arranged in antero-posterior and vertical direction on the shoulder of a garment. Gait patterns in continuous accelerometer records were classified in two steps. In the first step, direct spatial correlation of discrete dyadic wavelet coefficients was applied to separate the segments of gait patterns in the continuous accelerometer record. Compared to the reference system, averaged absolute error 0.387 s for ascending stairs and 0.404 s for descending stairs were achieved. The overall sensitivity and specificity of ascending stairs were 98.79% and 99.52%, and those of descending stairs were 97.35% and 99.62%. In the second step, powers of wavelet coefficients of 2 s time duration from separated segments of vertical and antero-posterior acceleration signals were used as features in classification. Our results proved a reliable technique of measuring gait patterns during physical activity.

Mesh:

Year:  2005        PMID: 16212968     DOI: 10.1016/j.jbiomech.2005.08.014

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  22 in total

1.  Understanding the effects of pre-processing on extracted signal features from gait accelerometry signals.

Authors:  Alexandre Millecamps; Kristin A Lowry; Jennifer S Brach; Subashan Perera; Mark S Redfern; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2015-04-04       Impact factor: 4.589

Review 2.  The Elderly's Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development.

Authors:  Qin Ni; Ana Belén García Hernando; Iván Pau de la Cruz
Journal:  Sensors (Basel)       Date:  2015-05-14       Impact factor: 3.576

3.  Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation.

Authors:  Marta Karas; Marcin Stra Czkiewicz; William Fadel; Jaroslaw Harezlak; Ciprian M Crainiceanu; Jacek K Urbanek
Journal:  Biostatistics       Date:  2021-04-10       Impact factor: 5.899

4.  Development and evaluation of a prior-to-impact fall event detection algorithm.

Authors:  Jian Liu; Thurmon E Lockhart
Journal:  IEEE Trans Biomed Eng       Date:  2014-04-04       Impact factor: 4.538

5.  A method to estimate free-living active and sedentary behavior from an accelerometer.

Authors:  Kate Lyden; Sarah Kozey Keadle; John Staudenmayer; Patty S Freedson
Journal:  Med Sci Sports Exerc       Date:  2014-02       Impact factor: 5.411

6.  Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.

Authors:  Vipul Lugade; Emma Fortune; Melissa Morrow; Kenton Kaufman
Journal:  Med Eng Phys       Date:  2013-07-27       Impact factor: 2.242

7.  A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains.

Authors:  Ervin Sejdić; Kristin A Lowry; Jennica Bellanca; Mark S Redfern; Jennifer S Brach
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-06-06       Impact factor: 3.802

8.  Evaluating the Performance of Sensor-based Bout Detection Algorithms: The Transition Pairing Method.

Authors:  Paul R Hibbing; Samuel R LaMunion; Haileab Hilafu; Scott E Crouter
Journal:  J Meas Phys Behav       Date:  2020-05-20

9.  A wavelet-based approach to fall detection.

Authors:  Luca Palmerini; Fabio Bagalà; Andrea Zanetti; Jochen Klenk; Clemens Becker; Angelo Cappello
Journal:  Sensors (Basel)       Date:  2015-05-20       Impact factor: 3.576

10.  Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset.

Authors:  Heike Leutheuser; Dominik Schuldhaus; Bjoern M Eskofier
Journal:  PLoS One       Date:  2013-10-09       Impact factor: 3.240

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