Sports Medicine

, Volume 47, Issue 3, pp 439–447 | Cite as

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

  • Cain C. T. Clark
  • Claire M. Barnes
  • Gareth Stratton
  • Melitta A. McNarry
  • Kelly A. Mackintosh
  • Huw D. Summers
Review Article


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.


  1. 1.
    World Health Organization. Global recommendations on physical activity for health. Geneva: WHO [online]. Available from: Accessed 6 July 2016.
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. 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.Google Scholar
  6. 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.CrossRefPubMedGoogle Scholar
  7. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 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.CrossRefPubMedGoogle Scholar
  9. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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.CrossRefPubMedGoogle Scholar
  11. 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.CrossRefPubMedGoogle Scholar
  12. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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.CrossRefPubMedGoogle Scholar
  14. 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.CrossRefPubMedGoogle Scholar
  15. 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.CrossRefPubMedGoogle Scholar
  16. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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.Google Scholar
  20. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 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.CrossRefPubMedGoogle Scholar
  25. 25.
    Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87(9):1423–47.CrossRefGoogle Scholar
  26. 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.CrossRefPubMedGoogle Scholar
  27. 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.CrossRefPubMedGoogle Scholar
  28. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cain C. T. Clark
    • 1
    • 3
  • Claire M. Barnes
    • 2
    • 3
  • Gareth Stratton
    • 1
    • 3
  • Melitta A. McNarry
    • 1
    • 3
  • Kelly A. Mackintosh
    • 1
    • 3
  • Huw D. Summers
    • 2
    • 3
  1. 1.Applied Sports Technology, Exercise and Medicine (A-STEM) Research centre, College of EngineeringSwansea UniversitySwanseaWales
  2. 2.Centre for Nanohealth, College of EngineeringSwansea UniversitySwanseaWales
  3. 3.Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of EngineeringSwansea UniversitySwanseaWales

Personalised recommendations