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Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers

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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Comparison of Three Algorithms Using Thigh-Worn Accelerometers for Classifying Sitting, Standing, and Stepping in Free-Living Office Workers

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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The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement

Acti4 software. Results: During workdays with lifting compared to workdays without lifting we observed non-significantly higher intensity of occupational physical activity (Δ 3.75 % heart rate reserve), number of steps/workday (Δ 4,346 steps), standing/walking activities (Δ 78 min/workday), as well as

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Where to Place Which Sensor to Measure Sedentary Behavior? A Method Development and Comparison Among Various Sensor Placements and Signal Types

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