A Transparent Method for Step Detection Using an Acceleration Threshold

in Journal for the Measurement of Physical Behaviour

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Scott W. DucharmeDepartment of Kinesiology, California State University, Long Beach, Long Beach, CA, USA

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Jongil LimDepartment of Counseling, Health and Kinesiology, Texas A&M University-San Antonio, San Antonio, TX, USA

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Michael A. BusaInstitute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA

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Elroy J. AguiarDepartment of Kinesiology, The University of Alabama, Tuscaloosa, AL, USA

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Christopher C. MooreDepartment of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

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John M. Schuna Jr.School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR, USA

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Tiago V. BarreiraFalk College, Syracuse University, Syracuse, NY, USA

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John StaudenmayerDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, USA

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Stuart R. ChipkinDepartment of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA

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Catrine Tudor-LockeCollege of Health and Human Services, The University of North Carolina at Charlotte, Charlotte, NC, USA

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DOI:
https://doi.org/10.1123/jmpb.2021-0011
Keywords:
physical activity monitor; accelerometer; step algorithm; physical activity; wearable devices
First Published Online:
25 Oct 2021
In Print:
Volume 4: Issue 4
Page Range:
311–320
Restricted access

Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply does not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and nonambulatory activities. Seventy-five adults (21–39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22 to 2.2 m/s. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their nondominant wrist. Raw acceleration (g) signals from the anterior–posterior, medial–lateral, vertical, and vector magnitude directions were assessed separately for each device. Signals were demeaned across all activities and band-pass filtered (0.25, 2.5 Hz). Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold = 0.0267g) and wrist (threshold = 0.0359g) using the vector magnitude signal. These thresholds yielded low error for the waist (RMSE < 173 steps, ≤2.28 steps/min) and wrist (RMSE < 481 steps, ≤6.47 steps/min) across all activities, and outperformed ActiLife’s proprietary algorithm (RMSE = 1,312 and 2,913 steps, 17.29 and 38.06 steps/min for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.

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  • 2018 Physical Activity Guidelines Advisory Committee Scientific Report. (2018). Washington, DC: U.S. Department of Health and Human Services.

    • Search Google Scholar
    • Export Citation

  • Bassett, D.R., Jr., Toth, L.P., LaMunion, S.R., & Crouter, S.E. (2017). Step counting: A review of measurement considerations and health-related applications. Sports Medicine, 47(7), 1303–1315. https://doi.org/10.1007/s40279-016-0663-1

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Borg, G.A.V. (1982). Psychophysical bases of perceived exertion. Medicine & Science in Sports & Exercise, 14(5), 377–381. https://doi.org/10.1249/00005768-198205000-00012

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). (2011–2014). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Center for Disease Control and Prevention. https://www.cdc.gov/nchs/nhanes/continousnhanes/default.aspx?BeginYear=2011

    • Search Google Scholar
    • Export Citation

  • Chow, J.J., Thom, J.M., Wewege, M.A., Ward, R.E., & Parmenter, B.J. (2017). Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site. Gait & Posture, 57, 199–203. https://doi.org/10.1016/j.gaitpost.2017.06.012

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Del Din, S., Godfrey, A., & Rochester, L. (2016). Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson’s disease: Toward clinical and at home use. IEEE Journal of Biomedical and Health Informatics, 20(3), 838–847. https://doi.org/10.1109/JBHI.2015.2419317

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dijkstra, B., Kamsma, Y.P., & Zijlstra, W. (2010). Detection of gait and postures using a miniaturized triaxial accelerometer-based system: Accuracy in patients with mild to moderate Parkinson’s disease. Archives of Physical Medicine and Rehabilitation, 91(8), 1272–1277. https://doi.org/10.1016/j.apmr.2010.05.004

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Evenson, K.R., Wen, F., Herring, A.H., Di, C., LaMonte, M.J., Tinker, L.F., Lee, I.M., Rillamas-Sun, E., LaCroix, A.Z., & Buchner, D.M. (2015). Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: The Women’s Health Initiative OPACH Calibration Study. Preventive Medicine Reports, 2, 750–756. https://doi.org/10.1016/j.pmedr.2015.08.021

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fortune, E., Lugade, V.A., Amin, S., & Kaufman, K.R. (2015). Step detection using multi- versus single tri-axial accelerometer-based systems. Physiological Measurement, 36(12), 2519–2535. https://doi.org/10.1088/0967-3334/36/12/2519

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fortune, E., Lugade, V., Morrow, M., & Kaufman, K. (2014). Validity of using tri-axial accelerometers to measure human movement—Part II: Step counts at a wide range of gait velocities. Medical Engineering & Physics, 36(6), 659–669. https://doi.org/10.1016/j.medengphy.2014.02.006

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Godfrey, A., Del Din, S., Barry, G., Mathers, J.C., & Rochester, L. (2015). Instrumenting gait with an accelerometer: A system and algorithm examination. Medical Engineering & Physics, 37(4), 400–407. https://doi.org/10.1016/j.medengphy.2015.02.003

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hall, K.S., Hyde, E.T., Bassett, D.R., Carlson, S.A., Carnethon, M.R., Ekelund, U., Evenson, K.R., Galuska, D.A., Kraus, W.E., Lee, I.M., Matthews, C.E., Omura, J.D., Paluch, A.E., Thomas, W.I., & Fulton, J.E. (2020). Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. The International Journal of Behavioral Nutrition and Physical Activity, 17, 78. https://doi.org/10.1186/s12966-020-00978-9

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hickey, A., Del Din, S., Rochester, L., & Godfrey, A. (2017). Detecting free-living steps and walking bouts: Validating an algorithm for macro gait analysis. Physiological Measurement, 38(1), N1–N15. https://doi.org/10.1088/1361-6579/38/1/N1

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hickey, A., John, D., Sasaki, J.E., Mavilia, M., & Freedson, P. (2016). Validity of activity monitor step detection is related to movement patterns. Journal of Physical Activity and Health, 13(2), 145–153. https://doi.org/10.1123/jpah.2015-0203

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hulteen, R.M., Smith, J.J., Morgan, P.J., Barnett, L.M., Hallal, P.C., Colyvas, K., & Lubans, D.R. (2017). Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Preventive Medicine, 95, 14–25. https://doi.org/10.1016/j.ypmed.2016.11.027

    • Crossref
    • Search Google Scholar
    • Export Citation

  • John, D., & Freedson, P. (2012). ActiGraph and Actical physical activity monitors: A peek under the hood. Medicine & Science in Sports & Exercise, 44(1, Suppl. 1), S86–S89. https://doi.org/10.1249/MSS.0b013e3182399f5e

    • Crossref
    • Search Google Scholar
    • Export Citation

  • John, D., Sasaki, J., Staudenmayer, J., Mavilia, M., & Freedson, P.S. (2013). Comparison of raw acceleration from the GENEA and ActiGraph GT3X+ activity monitors. Sensors, 13(11), 14754–14763. https://doi.org/10.3390/s131114754

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kraus, W.E., Janz, K.F., Powell, K.E., Campbell, W.W., Jakicic, J.M., Troiano, R.P., Sprow, K., Torres, A., Piercy, K.L., & 2018 Physical Activity Guidelines Advisory Committee. (2019). Daily step counts for measuring physical activity exposure and its relation to health. Medicine & Science in Sports & Exercise, 51(6), 1206–1212. https://doi.org/10.1249/MSS.0000000000001932

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McCamley, J., Donati, M., Grimpampi, E., & Mazza, C. (2012). An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait & Posture, 36(2), 316–318. https://doi.org/10.1016/j.gaitpost.2012.02.019

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Menz, H.B., Lord, S.R., & Fitzpatrick, R.C. (2003). Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait & Posture, 18(1), 35–46. https://doi.org/10.1016/S0966-6362(02)00159-5

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Migueles, J.H., Cadenas-Sanchez, C., Ekelund, U., Delisle Nystrom, C., Mora-Gonzalez, J., Lof, M., Labayen, I., Ruiz, J.R., & Ortega, F.B. (2017). Accelerometer data collection and processing criteria to assess physical activity and other outcomes: A systematic review and practical considerations. Sports Medicine, 47(9), 1821–1845. https://doi.org/10.1007/s40279-017-0716-0

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moore, C.C., McCullough, A.K., Aguiar, E.J., Ducharme, S.W., & Tudor-Locke, C. (2020). Toward harmonized treadmill-based validation of step-counting wearable technologies: A scoping review. Journal of Physical Activity and Health, 17(8), 840–852. https://doi.org/10.1123/jpah.2019-0205

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Munoz-Organero, M., & Ruiz-Blazquez, R. (2017). Detecting steps walking at very low speeds combining outlier detection, transition matrices and autoencoders from acceleration patterns. Sensors, 17, 2274. https://doi.org/10.3390/s17102274

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pham, M.H., Elshehabi, M., Haertner, L., Del Din, S., Srulijes, K., Heger, T., Synofzik, M., Hobert, M.A., Faber, G.S., Hansen, C., Salkovic, D., Ferreira, J.J., Berg, D., Sanchez-Ferro, A., van Dieen, J.H., Becker, C., Rochester, L., Schmidt, G., & Maetzler, W. (2017). Validation of a step detection algorithm during straight walking and turning in patients with Parkinson’s disease and older adults using an inertial measurement unit at the lower back. Frontiers in Neurology, 8, 457. https://doi.org/10.3389/fneur.2017.00457

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Staudenmayer, J., Zhu, W., & Catellier, D.J. (2012). Statistical considerations in the analysis of accelerometry-based activity monitor data. Medicine & Science in Sports & Exercise, 44(Suppl. 1), S61–S67. https://doi.org/10.1249/MSS.0b013e3182399e0f

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Toth, L.P., Park, S., Springer, C.M., Feyerabend, M.D., Steeves, J.A., & Bassett, D.R. (2018). Video-recorded validation of wearable step counters under free-living conditions. Medicine & Science in Sports & Exercise, 50(6), 1315–1322. https://doi.org/10.1249/mss.0000000000001569

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., Aguiar, E.J., Han, H., Ducharme, S.W., Schuna, J.M., Jr., Barreira, T.V., Moore, C.C., Busa, M.A., Lim, J., Sirard, J.R., Chipkin, S.R., & Staudenmayer, J. (2019). Walking cadence (steps/min) and intensity in 21-40 year olds: CADENCE-adults. The International Journal of Behavioral Nutrition and Physical Activity, 16(1), 8. https://doi.org/10.1186/s12966-019-0769-6

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., Camhi, S.M., Leonardi, C., Johnson, W.D., Katzmarzyk, P.T., Earnest, C.P., & Church, T.S. (2011). Patterns of adult stepping cadence in the 2005-2006 NHANES [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S.]. Preventive Medicine, 53(3), 178–181. https://doi.org/10.1016/j.ypmed.2011.06.004

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., & Rowe, D.A. (2012). Using cadence to study free-living ambulatory behaviour. Sports Medicine, 42(5), 381–398. https://doi.org/10.2165/11599170-000000000-00000

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vaha-Ypya, H., Husu, P., Suni, J., Vasankari, T., & Sievanen, H. (2018). Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer. Scandinavian Journal of Medicine & Science in Sports, 28(3), 1092–1102. https://doi.org/10.1111/sms.13017

    • Crossref
    • Search Google Scholar
    • Export Citation

  • van Hees, V.T., Thaler-Kall, K., Wolf, K.H., Brond, J.C., Bonomi, A., Schulze, M., Vigl, M., Morseth, B., Arnesdatter Hopstock, L., Gorzelniak, L., Schulz, H., Brage, S., & Horsch, A. (2016). Challenges and opportunities for harmonizing research methodology: Raw accelerometry. Methods of Information in Medicine, 55(6), 525–532. https://doi.org/10.3414/ME15-05-0013

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Van Loo, C.M.T., Okely, A.D., Batterham, M.J., Hinkley, T., Ekelund, U., Brage, S., Reilly, J.J., Trost, S.G., Jones, R.A., Janssen, X., & Cliff, D.P. (2018). Wrist acceleration cut points for moderate-to-vigorous physical activity in youth. Medicine & Science in Sports & Exercise, 50(3), 609–616. https://doi.org/10.1249/MSS.0000000000001449

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Welk, G.J. (2019). Harmonizing monitor- and report-based estimates of physical activity through calibration. Kinesiology Review, 8(1), 16–24. https://doi.org/10.1123/kr.2018-0064

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Welk, G.J., Bai, Y., Lee, J.M., Godino, J., Saint-Maurice, P.F., & Carr, L. (2019). Standardizing analytic methods and reporting in activity monitor validation studies. Medicine & Science in Sports & Exercise, 51(8), 1767–1780. https://doi.org/10.1249/MSS.0000000000001966

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Westerterp, K.R. (2009). Assessment of physical activity: A critical appraisal. European Journal of Applied Physiology, 105(6), 823–828. https://doi.org/10.1007/s00421-009-1000-2

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zijlstra, W., & Hof, A.L. (2003). Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & Posture, 18(2), 1–10. https://doi.org/10.1016/S0966-6362(02)00190-X

    • Crossref
    • Search Google Scholar
    • Export Citation
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