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Jørgen Skotte, Mette Korshøj, Jesper Kristiansen, Christiana Hanisch, and Andreas Holtermann


The aim of this study was to validate a triaxial accelerometer setup for identifying everyday physical activity types (ie, sitting, standing, walking, walking stairs, running, and cycling).


Seventeen subjects equipped with triaxial accelerometers (ActiGraph GT3X+) at the thigh and hip carried out a standardized test procedure including walking, running, cycling, walking stairs, sitting, and standing still. A method was developed (Acti4) to discriminate between these physical activity types based on threshold values of standard deviation of acceleration and the derived inclination. Moreover, the ability of the accelerometer placed at the thigh to detect sitting posture was separately validated during free living by comparison with recordings of pressure sensors in the hip pockets.


Sensitivity for discriminating between the physical activity types sitting, standing, walking, running, and cycling in the standardized trials were 99%–100% and 95% for walking stairs. Specificity was higher than 99% for all activities. During free living (140 hours of measurements), sensitivity and specificity for detection of sitting posture were 98% and 93%, respectively.


The developed method for detecting physical activity types showed a high sensitivity and specificity for sitting, standing, walking, running, walking stairs, and cycling in a standardized setting and for sitting posture during free living.

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Kristin Suorsa, Anna Pulakka, Tuija Leskinen, Jaana Pentti, Andreas Holtermann, Olli J. Heinonen, Juha Sunikka, Jussi Vahtera, and Sari Stenholm

been reported especially for data processing algorithms in the PAL Technologies software designed for thigh-worn activPAL accelerometers ( Kozey Keadle et al., 2011 ; Lyden et al., 2012 ), and in the Acti4 software ( Kongsvold, 2016 ; Skotte et al., 2014 ; Stemland et al., 2015 ), which can be used

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Bronwyn Clark, Elisabeth Winker, Matthew Ahmadi, and Stewart Trost

work place intervention studies, such as walking and stair climbing. Applying machine learning methods, Skotte and colleagues ( Skotte, Korshøj, Kristiansen, Hanisch, & Holtermann, 2014 ) trained a decision tree classification algorithm, described as the Acti4 method, for the automatic detection of

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Billy C.L. So, Sze C. Kwok, and Paul H. Lee

trial . Int J Behav Med . 2016 ; 23 ( 4 ): 501 – 506 . PubMed ID: 26025630 10.1007/s12529-015-9492-0 26025630 12. Korshøj M , Skotte JH , Christiansen CS , et al . Validity of the Acti4 software using ActiGraph GT3X+accelerometer for recording of arm and upper body inclination in simulated

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Roman P. Kuster, Daniel Baumgartner, Maria Hagströmer, and Wilhelmus J.A. Grooten

.2015 Stemland , I. , Ingebrigtsen , J. , Christiansen , C.S. , Jensen , B.R. , Hanisch , C. , Skotte , J. , & Holtermann , A. ( 2015 ). Validity of the Acti4 method for detection of physical activity types in free-living settings: Comparison with video analysis . Ergonomics, 58 ( 6 ), 953

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Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan, and Lisa Mackay

 al . Validity of the Acti4 method for detection of physical activity types in free-living settings: comparison with video analysis . Ergonomics . 2015 ; 58 ( 6 ): 953 – 965 . PubMed ID: 25588819 doi:10.1080/00140139.2014.998724 10.1080/00140139.2014.998724 25588819 15. de Almeida Mendes M , da Silva ICM