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Comparing Accelerometer Processing Metrics and Hyperparameter Optimization for Physical Activity Classification Accuracy Using Machine Learning Methods

Sumayyah Bamidele Musa, Arnab Barua, Kevin G. Stanley, Fabien A. Basset, Hiroshi Mamyia, Kevin Mongeon, and Daniel Fuller

Background: Physical activity (PA) is a crucial factor in maintaining good health and preventing chronic diseases. However, accurately measuring PA is challenging. Euclidean Norm Minus One (ENMO), ActiGraph Counts, and Monitor-Independent Movement Summary (MIMS) units are processing metrics used to classify PA through accelerometry, but they employ different methods to calculate activity levels. This study aimed to compare ENMO, ActiGraph Counts, and MIMS accelerometer metrics using machine learning algorithms. Methods: Data from a smartphone accelerometer were collected from 50 participants who held the smartphone in their right hand while completing six activities. The data were used to generate ENMO, ActiGraph Counts, and MIMS acceleration metrics. Random Forest, K-Nearest Neighbor, and Support Vector Machine algorithms were applied to the data to classify PA into different levels of activity intensity and types. The algorithms’ performance was evaluated using various metrics such as accuracy, precision, and recall. Results: The results showed that both the Random Forest and K-Nearest Neighbor algorithms performed well, achieving above 80% accuracy in classifying PA into different intensity levels and types. Both the ENMO and MIMS metrics proved more accurate than ActiGraph Counts in classifying moderate to vigorous PA. Conclusions: This study provides evidence that both ENMO and MIMS metrics can accurately measure PA with accelerometry, and machine learning algorithms can classify the activity into different intensity levels. These metrics and methods are valuable tools for monitoring PA and understanding the relationship between PA and health outcomes.

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Moving Beyond the Characterization of Activity Intensity Bouts as Square Waves Signals

Myles W. O’Brien, Jennifer L. Petterson, Liam P. Pellerine, Madeline E. Shivgulam, Derek S. Kimmerly, Ryan J. Frayne, Pasan Hettiarachchi, and Peter J. Johansson

status, age, and sex may impact the V ˙ O 2 kinetics during exercise, as reviewed in detail by Poole and Jones ( 2012 ). Accordingly, the findings of the experimental portion of the study may be applicable to young, healthy adults, but the slower V ˙ O 2 responses in older adults ( Bell et al., 1999

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Validation of Body-Worn Sensors for Gait Analysis During a 2-min Walk Test in Children

Vincent Shieh, Cris Zampieri, Ashwini Sansare, John Collins, Thomas C. Bulea, and Minal Jain

to measure limb kinematics, spatiotemporal gait parameters, and even joint kinetics, and muscle activity during walking. However, motion capture systems for gait evaluation are generally restricted to larger facilities due to high cost, space, and staffing requirements. While the measurements are

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Evolution of Public Health Physical Activity Applications of Accelerometers: A Personal Perspective

Richard P. Troiano

2 —Conceptual framework from “Framework for Physical Activity as a Complex and Multidimensional Behavior,” by K.K. Pettee Gabriel and J.R. Morrow Jr., 2012, Journal of Physical Activity & Health, 9 (Suppl. 1), S11–S18 ( https://doi.org/10.1123/jpah.9.s1.s11 ). Copyright 2012 by Human Kinetics. I

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Impact of Continuous Focal Sampling Time Thresholds on Physical Activity Metrics When Using Video-Recorded Direct Observation

Julian Martinez, John Staudenmayer, and Scott J. Strath

observation to assess physical activity . In G. Welk (Ed.), Physical activity assessments for health-related research (pp.  179 – 195 ). Human Kinetics . McKenzie , T.L. , Crespo , N.C. , Baquero , B. , & Elder , J.P. ( 2010 ). Leisure-time physical activity in elementary schools: Analysis of

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Accelerometry and Self-Report Are Congruent for Children’s Moderate-to-Vigorous and Higher Intensity Physical Activity

Claudio R. Nigg, Xanna Burg, Barbara Lohse, and Leslie Cunningham-Sabo

.C. , & Kamata , A. (Eds.), Measurement in Sport and Exercise Psychology . Champaign, IL: Human Kinetics , 455 – 464 . Nigg , C.R. , Kutchman , E. , Amato , K. , Schaefer , C.A. , Zhang , G. , Anwar , M.U. , … Hill , J. ( 2018 ). Recess environment and curriculum intervention on children

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Calibration of an Accelerometer Activity Index Among Older Women and Its Association With Cardiometabolic Risk Factors

Guangxing Wang, Sixuan Wu, Kelly R. Evenson, Ilsuk Kang, Michael J. LaMonte, John Bellettiere, I-Min Lee, Annie Green Howard, Andrea Z. LaCroix, and Chongzhi Di

. https://doi.org/10.1186/s12889-017-4065-6 10.1186/s12889-017-4065-6 LaMonte , M. , Ainsworth , B. , & Reis , J. ( 2006 ). Measuring physical activity . In T. Wood & W. Zhu (Eds.), Measurement theory and practice in kinesiology (pp.  237 – 271 ). Human Kinetics . LaMonte , M

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Simultaneous Validation of Count-to-Activity Thresholds for Five Commonly Used Activity Monitors in Adolescent Research: A Step Toward Data Harmonization

Gráinne Hayes, Kieran Dowd, Ciaran MacDonncha, and Alan Donnely

.R. , Derion , T. , Kennedy , B. , Guy , H.J. , Prediletto , R. , & Wagner , P.D. ( 1991 ). Contribution of excising legs to the slow component of oxygen uptake kinetics in humans . Journal of Applied Physiology, 71 ( 4 ), 1245 – 1260 . https://doi.org/10.1152/jappl.1991.71.4.1245 10.1152/jappl

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Reliability and Criterion-Related Validity of the activPAL Accelerometer When Measuring Physical Activity and Sedentary Behavior in Adults With Lower Limb Absence

Sarah Deans, Alison Kirk, Anthony McGarry, and David Rowe

. Wood & W. Zhu (Eds.), Measurement theory and practice in kinesiology (pp.  9 – 26 ). Champaign, IL : Human Kinetics . Ryan , C.G. , Grant , P.M. , Tigbe , W.W. , & Granat , M.H. ( 2006 ). The validity and reliability of a novel activity monitor as a measure of walking . British

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Association of Individual Motor Abilities and Accelerometer-Derived Physical Activity Measures in Preschool-Aged Children

Becky Breau, Berit Brandes, Marvin N. Wright, Christoph Buck, Lori Ann Vallis, and Mirko Brandes

preschool children: A cross-sectional and longitudinal study (ballabeina) . International Journal of Obesity, 35 ( 7 ), 937 – 944 . PubMed ID: 21448128 doi:10.1038/ijo.2011.54 10.1038/ijo.2011.54 Burton , A. , & Miller , D. ( 1998 ). Movement skill assessment . Champaign, IL : Human Kinetics