Literature DB >> 22617400

Activity classification using the GENEA: optimum sampling frequency and number of axes.

Shaoyan Zhang1, Peter Murray, Ruediger Zillmer, Roger G Eston, Michael Catt, Alex V Rowlands.   

Abstract

INTRODUCTION: The GENEA shows high accuracy for classification of sedentary, household, walking, and running activities when sampling at 80 Hz on three axes. It is not known whether it is possible to decrease this sampling frequency and/or the number of axes without detriment to classification accuracy. The purpose of this study was to compare the classification rate of activities on the basis of data from a single axis, two axes, and three axes, with sampling rates ranging from 5 to 80 Hz.
METHODS: Sixty participants (age, 49.4 yr (6.5 yr); BMI, 24.6 kg·m (3.4 kg·m)) completed 10-12 semistructured activities in the laboratory and outdoor environment while wearing a GENEA accelerometer on the right wrist. We analyzed data from single axis, dual axes, and three axes at sampling rates of 5, 10, 20, 40, and 80 Hz. Mathematical models based on features extracted from mean, SD, fast Fourier transform, and wavelet decomposition were built, which combined one of the numbers of axes with one of the sampling rates to classify activities into sedentary, household, walking, and running.
RESULTS: Classification accuracy was high irrespective of the number of axes for data collected at 80 Hz (96.93% ± 0.97%), 40 Hz (97.4% ± 0.73%), 20 Hz (96.86% ± 1.12%), and 10 Hz (97.01% ± 1.01%) but dropped for data collected at 5 Hz (94.98% ± 1.36%).
CONCLUSION: Sampling frequencies >10 Hz and/or more than one axis of measurement were not associated with greater classification accuracy. Lower sampling rates and measurement of a single axis would result in a lower data load, longer battery life, and higher efficiency of data processing. Further research should investigate whether a lower sampling rate and a single axis affects classification accuracy when considering a wider range of activities.

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Year:  2012        PMID: 22617400     DOI: 10.1249/MSS.0b013e31825e19fd

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  16 in total

1.  Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer.

Authors:  Whitney A Welch; David R Bassett; Dixie L Thompson; Patty S Freedson; John W Staudenmayer; Dinesh John; Jeremy A Steeves; Scott A Conger; Tyrone Ceaser; Cheryl A Howe; Jeffer E Sasaki; Eugene C Fitzhugh
Journal:  Med Sci Sports Exerc       Date:  2013-10       Impact factor: 5.411

2.  Assessment of Physical Activity of Hospitalised Older Adults: A Systematic Review.

Authors:  S E R Lim; K Ibrahim; A A Sayer; H C Roberts
Journal:  J Nutr Health Aging       Date:  2018       Impact factor: 4.075

3.  Performance of Activity Classification Algorithms in Free-Living Older Adults.

Authors:  Jeffer Eidi Sasaki; Amanda M Hickey; John W Staudenmayer; Dinesh John; Jane A Kent; Patty S Freedson
Journal:  Med Sci Sports Exerc       Date:  2016-05       Impact factor: 5.411

4.  Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors.

Authors:  Séverine Sabia; Vincent T van Hees; Martin J Shipley; Michael I Trenell; Gareth Hagger-Johnson; Alexis Elbaz; Mika Kivimaki; Archana Singh-Manoux
Journal:  Am J Epidemiol       Date:  2014-02-04       Impact factor: 4.897

5.  Long-term activity recognition from wristwatch accelerometer data.

Authors:  Enrique Garcia-Ceja; Ramon F Brena; Jose C Carrasco-Jimenez; Leonardo Garrido
Journal:  Sensors (Basel)       Date:  2014-11-27       Impact factor: 3.576

Review 6.  Current Physical Activity Monitors in Hip and Knee Osteoarthritis: A Review.

Authors:  Maik Sliepen; Mirko Brandes; Dieter Rosenbaum
Journal:  Arthritis Care Res (Hoboken)       Date:  2017-09-06       Impact factor: 4.794

7.  Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners.

Authors:  Carlos Hernando; Carla Hernando; Eladio Joaquin Collado; Nayara Panizo; Ignacio Martinez-Navarro; Barbara Hernando
Journal:  PLoS One       Date:  2018-08-29       Impact factor: 3.240

8.  Comparison of self-reported and accelerometer-assessed measurements of physical activity according to socio-demographic characteristics in Korean adults.

Authors:  Seung Won Lee; Jee-Seon Shim; Bo Mi Song; Ho Jae Lee; Hye Yoon Bae; Ji Hye Park; Hye Rin Choi; Jae Won Yang; Ji Eun Heo; So Mi Jemma Cho; Ga Bin Lee; Diana Huanan Hidalgo; Tae-Hoon Kim; Kyung Soo Chung; Hyeon Chang Kim
Journal:  Epidemiol Health       Date:  2018-11-29

9.  Current clinical utilisation of wearable motion sensors for the assessment of outcome following knee arthroplasty: a scoping review.

Authors:  Scott R Small; Garrett S Bullock; Sara Khalid; Karen Barker; Marialena Trivella; Andrew James Price
Journal:  BMJ Open       Date:  2019-12-29       Impact factor: 2.692

10.  Associations Between Objectively Measured Physical Activity, Body Composition and Sarcopenia: Findings from the Hertfordshire Sarcopenia Study (HSS).

Authors:  Leo D Westbury; Richard M Dodds; Holly E Syddall; Alicja M Baczynska; Sarah C Shaw; Elaine M Dennison; Helen C Roberts; Avan Aihie Sayer; Cyrus Cooper; Harnish P Patel
Journal:  Calcif Tissue Int       Date:  2018-03-27       Impact factor: 4.333

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