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A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans

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Abstract

Physical inactivity is one of the most prevalent risk factors for non-communicable diseases in the world. A fundamental barrier to enhancing physical activity levels and decreasing sedentary behavior is limited by our understanding of associated measurement and analytical techniques. The number of analytical techniques for physical activity measurement has grown significantly, and although emerging techniques may advance analyses, little consensus is presently available and further synthesis is therefore required. The objective of this review was to identify the accuracy of emerging analytical techniques used for physical activity measurement in humans. We conducted a search of electronic databases using Web of Science, PubMed, and Google Scholar. This review included studies written in English and published between January 2010 and December 2014 that assessed physical activity using emerging analytical techniques and reported technique accuracy. A total of 2064 papers were initially retrieved from three databases. After duplicates were removed and remaining articles screened, 50 full-text articles were reviewed, resulting in the inclusion of 11 articles that met the eligibility criteria. Despite the diverse nature and the range in accuracy associated with some of the analytic techniques, the rapid development of analytics has demonstrated that more sensitive information about physical activity may be attained. However, further refinement of these techniques is needed.

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References

  1. World Health Organization. Global recommendations on physical activity for health. Geneva: WHO [online]. Available from: http://www.who.int/dietphysicalactivity/publications/pa/en/index.html. Accessed 6 July 2016.

  2. Aminian K, Najafi B. Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications. Comput Animat Virtual Worlds. 2004;15(2):79–94. doi:10.1002/Cav.2.

    Article  Google Scholar 

  3. Zijlstra W, Aminian K. Mobility assessment in older people: new possibilities and challenges. Eur J Ageing. 2007;4(1):3–12. doi:10.1007/s10433-007-0041-9.

    Article  Google Scholar 

  4. Mathie MJ, Coster AC, Lovell NH, et al. Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol Meas. 2004;25(2):R1–20.

    Article  PubMed  Google Scholar 

  5. Van Hees V, Gorzelniak L, Leon E, et al. A method to compare new and traditional accelerometry data in physical activity monitoring. Montreal, QC: World of Wireless Mobile and Multimedia Networks (WoWMoM); 2012.

  6. Mattocks C, Leary S, Ness A, et al. Calibration of an accelerometer during free-living activities in children. Int J Pediatr Obes. 2007;2(4):218–26. doi:10.1080/17477160701408809.

    Article  PubMed  Google Scholar 

  7. van Hees VT, Gorzelniak L, Dean Leon EC, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013;8(4):e61691. doi:10.1371/journal.pone.0061691.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chen KY, Bassett DR Jr. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc. 2005;37(11 Suppl):S490–500.

    Article  PubMed  Google Scholar 

  9. Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel). 2010;10(8):7772–88. doi:10.3390/s100807772.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Preece SJ, Goulermas JY, Kenney LP, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng. 2009;56(3):871–9. doi:10.1109/TBME.2008.2006190.

    Article  PubMed  Google Scholar 

  11. Umstattd Meyer MR, Baller SL, Mitchell SM, et al. Comparison of 3 accelerometer data reduction approaches, step counts, and 2 self-report measures for estimating physical activity in free-living adults. J Phys Act Health. 2013;10(7):1068–74.

    Article  PubMed  Google Scholar 

  12. Leutheuser H, Schuldhaus D, Eskofier BM. Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS One. 2013;8(10):e75196. doi:10.1371/journal.pone.0075196.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Siervo M, Bertoli S, Battezzati A, et al. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin Nutr. 2014;33(4):613–9. doi:10.1016/j.clnu.2013.09.009.

    Article  CAS  PubMed  Google Scholar 

  14. Aziz O, Park EJ, Mori G, et al. Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers. Gait Posture. 2014;39(1):506–12. doi:10.1016/j.gaitpost.2013.08.034.

    Article  PubMed  Google Scholar 

  15. Bulling A, Ward JA, Gellersen H, et al. Eye movement analysis for activity recognition using electrooculography. IEEE Trans Pattern Anal Mach Intell. 2011;33(4):741–53. doi:10.1109/TPAMI.2010.86.

    Article  PubMed  Google Scholar 

  16. Duncan GE, Lester J, Migotsky S, et al. Accuracy of a novel multi-sensor board for measuring physical activity and energy expenditure. Eur J Appl Physiol. 2011;111(9):2025–32. doi:10.1007/s00421-011-1834-2.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Fulk GD, Sazonov E. Using sensors to measure activity in people with stroke. Top Stroke Rehabil. 2011;18(6):746–57. doi:10.1310/tsr1806-746.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Goncalves N, Rodrigues JL, Costa S, et al. Preliminary study on determining stereotypical motor movements. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:1598–601. doi:10.1109/EMBC.2012.6346250.

    PubMed  Google Scholar 

  19. Kjaergaard MB, Wirz M, Roggen D, et al (eds). Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones. New York, NY: Proc 2012 ACM Conference on Ubiquitous Computing; 2012.

  20. Mannini A, Sabatini AM. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors (Basel). 2010;10(2):1154–75. doi:10.3390/s100201154.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Trost SG, Wong WK, Pfeiffer KA, et al. Artificial neural networks to predict activity type and energy expenditure in youth. Med Sci Sports Exerc. 2012;44(9):1801–9. doi:10.1249/MSS.0b013e318258ac11.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Xiao ZG, Menon C. Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. J Neuroeng Rehabil. 2014;11(1):2. doi:10.1186/1743-0003-11-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhang H, Li L, Jia W, et al. Physical activity recognition based on motion in images acquired by a wearable camera. Neurocomputing. 2011;74(12–13):2184–92. doi:10.1016/j.neucom.2011.02.014.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Duncan S, White K, Sa’ulilo L, et al. Convergent validity of a piezoelectric pedometer and an omnidirectional accelerometer for measuring children’s physical activity. Pediatr Exerc Sci. 2011;23(3):399–410.

    Article  PubMed  Google Scholar 

  25. Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87(9):1423–47.

    Article  Google Scholar 

  26. Calabro MA, Stewart JM, Welk GJ. Validation of pattern-recognition monitors in children using doubly labeled water. Med Sci Sports Exerc. 2013;45(7):1313–22. doi:10.1249/MSS.0b013e31828579c3.

    Article  PubMed  Google Scholar 

  27. Staudenmayer J, Zhu W, Catellier DJ. Statistical considerations in the analysis of accelerometry-based activity monitor data. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S61–7. doi:10.1249/MSS.0b013e3182399e0f.

    Article  PubMed  Google Scholar 

  28. Braun E, Geurten B, Egelhaaf M. Identifying prototypical components in behaviour using clustering algorithms. Plos One. 2010;5(2):e9361. doi:10.1371/Journal.Pone.0009361.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tonkin JA, Rees P, Brown MR, et al. Automated cell identification and tracking using nanoparticle moving-light-displays. PLoS One. 2012;7(7):e40835. doi:10.1371/journal.pone.0040835.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Cain C. T. Clark.

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No sources of funding were used to assist in the preparation of this article.

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Cain C.T. Clark, Claire M. Barnes, Gareth Stratton, Melitta A. McNarry, Kelly A. Mackintosh, and Huw D. Summers have no conflicts of interest relevant to the content of this review.

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Clark, C.C.T., Barnes, C.M., Stratton, G. et al. A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans. Sports Med 47, 439–447 (2017). https://doi.org/10.1007/s40279-016-0585-y

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