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Matthew S. Tenan, Andrew J. Tweedell, and Courtney A. Haynes

movement. When considering surface EMG, this onset is commonly quantified via a linear envelope methodology proposed by David Winter. 1 Additional algorithmic approaches have also been validated for surface EMG onset detection, such as the Teager-Kaiser energy operator 2 and Sample Entropy. 3

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Ryan M. Chambers, Tim J. Gabbett, and Michael H. Cole

, snow sports, individual sports, and water sports. 11 Validated algorithms have been applied to microsensor data to automate the collection of sport-specific movements, such as fast bowling in cricket, 12 pitching in baseball, 13 and tackling in rugby. 11 , 14 , 15 To date, researchers have used

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Jordan A. Carlson, Fatima Tuz-Zahra, John Bellettiere, Nicola D. Ridgers, Chelsea Steel, Carolina Bejarano, Andrea Z. LaCroix, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Marta M. Jankowska, and Loki Natarajan

add participant, data collection, and processing burden, and the diary data are often incomplete due to challenges with compliance. More recently, automated algorithms have been created to estimate the awake wear time from activPAL data. A third-party algorithm, “ProcessingPAL,” classifies periods of

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Paul R. Hibbing, Samuel R. LaMunion, Haileab Hilafu, and Scott E. Crouter

, Schwabacher, Kim, Welk, & Cook, 2016 ; Hibbing, Ellingson, Dixon, & Welk, 2018 ; Lyden, Keadle, Staudenmayer, & Freedson, 2014 ). Thus, an alternative approach (dynamic segmentation) has been introduced ( Kozina, Luštrek, & Gams, 2011 ), whereby a bout detection algorithm is used to partition the data into

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Aisha Chen, Sandhya Selvaraj, Vennila Krishnan, and Shadnaz Asgari

applied a threshold equivalent to 10% of maximal COP velocity to calculate the onset. Nevertheless, many studies commonly use the tedious visual inspection of COP velocity or displacement to obtain the gait onset when the chosen algorithm fails to detect the correct onset. 20 , 21 , 25 Evaluation of both

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Nathalie Berninger, Gill ten Hoor, Guy Plasqui, and Rik Crutzen

needed, because additional information about sequential patterns of physical behaviors might explain additional variance in health outcomes, and it might, eventually, facilitate real-time and individualized feedback. Figure 1 The SPORT algorithm illustrated with segments of 2 days with identical number

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Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan

, Zderic, Schuna, Hamilton, & Tudor-Locke, 2015 ; Carlson et al., 2019 ). The desire for more accurate measurement of free-living behavior has led to alternate data processing techniques, such as machine learning (ML) algorithms. Numerous studies from the computer science domains have demonstrated the

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

harmonize accelerometer data processing methods ( Wijndaele et al., 2015 ), Edwardson and colleagues evaluated the accuracy of an open source algorithm for classifying posture from raw acceleration signals from an ActiGraph ™ GT3X+ (ActiGraph, LLC; Pensacola, FL) accelerometer positioned on the thigh

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Elif Inan-Eroglu, Bo-Huei Huang, Leah Shepherd, Natalie Pearson, Annemarie Koster, Peter Palm, Peter A. Cistulli, Mark Hamer, and Emmanuel Stamatakis

; Oliver, Badland, Shepherd, & Schofield, 2011 ), their uptake in sleep measurement studies is limited. Winkler et al. ( 2016 ) and van der Berg et al. ( 2016 ) have recently developed automated algorithms to isolate adults’ valid waking wear periods from thigh-worn activPAL data collected with a

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Oren Tirosh and W.A. Sparrow

Analysis of human gait requires accurate measurement of foot-ground contact, often determined using either foot-ground reaction force thresholds or kinematic data. This study examined the differences in calculating event times across five vertical force thresholds and validated a vertical acceleration-based algorithm as a measure of heel contact and toe-off. The experiment also revealed the accuracy in determining heel contact and toe-off when raw displacement/time data were smoothed using a range of digital filter cutoff frequencies. Four healthy young participants completed 10 walking trials: 5 at normal speed (1.2 m/s) and 5 at fast speed (1.8 m/s). A 3D optoelectric system was synchronized with a forceplate to measure the times when vertical force exceeded (heel contact) or fell below (toe-off) 10, 20, 30, 40, and 50 N. These were then compared and subsequently used to validate an acceleration-based method for calculating heel contact and toe-off with the displacement/time data filtered across a range of four cutoff frequencies. Linear regression analyses showed that during both normal and fast walking, any force threshold within 0 to 50 N could be used to predict heel-contact time. For estimating toe-off low force thresholds, 10 N or less should be used. When raw data were filtered with the optimal cutoff frequency, the absolute value (AbsDt) of the difference between the forceplate event times obtained using a 10-N threshold and the event times of heel contact and toe-off using the acceleration-based algorithms revealed average AbsDt of 10.0 and 16.5 ms for normal walking, and 7.4 and 13.5 ms for fast walking. Data smoothing with the non-optimal cutoff frequencies influenced the event times computed by the algorithms and produced greater AbsDt values. Optimal data filtering procedures are, therefore, essential for ensuring accurate measures of heel contact and toe-off when using the acceleration-based algorithms.