Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment

Jun Qi; Po Yang; Martin Hanneghan; Dina Fan; Zhikun Deng; Feng Dong

Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment

View Fulltext

Author(s): Jun Qi ¹ ; Po Yang ¹ ; Martin Hanneghan ¹ ; Dina Fan ² ; Zhikun Deng ² ; Feng Dong ²
- Affiliations: 1: Department of Computer Science, Liverpool John Moores University, Liverpool, UK ;
  2: Centre of Computer Graphics and Visualization, Bedfordshire University, Luton, UK
Source: Volume 5, Issue 5, September 2016, p. 107 – 113
DOI: 10.1049/iet-net.2015.0109 , Print ISSN 2047-4954, Online ISSN 2047-4962

Received 30/12/2015, Accepted 21/07/2016, Revised 17/05/2016, Published 01/09/2016

The popular use of wearable devices and mobile phones makes the effective capture of life-logging physical activity (PA) data in an Internet of Things (IoT) environment possible. The effective collection of measures of PA in the long term is beneficial to interdisciplinary healthcare research and collaboration from clinicians, researchers and patients. However, due to heterogeneity of connected devices and rapid change of diverse life patterns in an IoT environment, life-logging PA information captured by mobile devices usually contains much uncertainty. In this study, the authors project the distribution of irregular uncertainty by defining a walking speed related score named as daily activity in physical space and present an ellipse-fitting model-based validity improvement method for reducing uncertainties of life-logging PA measures in an IoT environment. The experimental results reflect that the proposed method remarkably improves the validity of PA measures in a healthcare platform.

References

1. 1)
  - 24. ‘Endomondo.’ Available at: https://www.endomondo.com/. [Accessed: 10-Apr-2015].
2. 2)
  - 19. Spanakis, E.G.: ‘MyHealthAvatar: personalized and empowerment health services through Internet of Things technologies MyHealthAvatar: personalized and empowerment health services through Internet of Things technologies’, no. August, 2015.
3. 3)
  - 9. Lyons, G.M., Culhane, K.M., Hilton, D., et al: ‘A description of an accelerometer-based mobility monitoring technique’, Med. Eng. Phys., 2005, 27, (6), pp. 497–504 (doi: 10.1016/j.medengphy.2004.11.006).
4. 4)
  - 25. ‘Moves.’ Available at: https://www.moves-app.com/. [Accessed: 14-Oct-2014].
5. 5)
  - 10. Yang, P., Wu, W., Moniri, M., et al: ‘Efficient object localization using sparsely distributed passive RFID tags’, IEEE Trans. Ind. Electron., 2013, 60, (12), pp. 5914–5924 (doi: 10.1109/TIE.2012.2230596).
6. 6)
  - 10. Lowe, S.A., ÓLaighin, G.: ‘Monitoring human health behaviour in one's living environment: a technological review’, Med. Eng. Phys., 2014, 36, (2), pp. 147–168 (doi: 10.1016/j.medengphy.2013.11.010).
7. 7)
  - 11. Altini, M., Penders, J., Vullers, R., et al: ‘Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning’, IEEE J. Biomed. Heal. Inf., 2014, 2194, (c), pp. 1–8.
8. 8)
  - 2. Minor, M.A., Hewett, J.E., Webel, R.R., et al: ‘Efficacy of physical conditioning exercise in patients with rheumatoid arthritis and osteoarthritis’, Arthritis Rheum., 1989, 32, (11), pp. 1396–1405 (doi: 10.1002/anr.1780321108).
9. 9)
  - 12. Dalton, A., OLaighin, G.: ‘A comparison of supervised learning techniques on the task of physical activity recognition’, IEEE Trans. Inf. Technol. Biomed., 2012, 17, (1), p. 1.
10. 10)
  - 7. Deng, Z., Yang, P., Zhao, Y., et al: ‘Life-logging data aggregation solution for interdisciplinary healthcare research and collaboration’. 2015 IEEE Int. Conf. Computer and Information Technology; Ubiquitous Computing Communications Dependable; Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 2315–2320.
11. 11)
  - 20. ‘Moves.’ Available at: https://www.moves-app.com/. [Accessed: 14-Apr-2015].
12. 12)
  - 26. Liu, S., Gao, R.X., John, D., et al: ‘Multisensor data fusion for physical activity assessment’, IEEE Trans. Biomed. Eng., 2012, 59, (3), pp. 687–696 (doi: 10.1109/TBME.2011.2178070).
13. 13)
  - 16. Tang, W., Sazonov, E.S.: ‘Highly accurate recognition of human postures and activities through classification with rejection’, IEEE J. Biomed. Heal. Inf., 2014, 18, (1), pp. 309–315 (doi: 10.1109/JBHI.2013.2287400).
14. 14)
  - 9. Karantonis, D.M., Narayanan, M.R., Mathie, M.: ‘Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring’, IEEE Trans. Inf.. Technol. Biomed., 2006, 10, (1), pp. 156–167 (doi: 10.1109/TITB.2005.856864).
15. 15)
  - 31. Po, Y., Wenyan, W.: ‘Efficient particle filter localisation algorithm in dense passive RFID tags environment’, IEEE Trans. Ind. Electron., 2014, 61, (10), pp. 5641–5651 (doi: 10.1109/TIE.2014.2301737).
16. 16)
  - 27. Mantyjarvi, J., Himberg, J., Seppanen, T.: ‘Recognizing human motion with multiple acceleration sensors’. 2001 IEEE Int. Conf. Systems, Man, and Cybernitics e-Systems e-Man Cybernitics Cybersp., 2001, vol. 2, pp. 2–7.
17. 17)
  - 23. ‘Withings.’ Available at: http://www.withings.com/uk/. [Accessed: 10-Apr-2015].
18. 18)
  - 18. ‘MHA.’ Available at: http://www.myhealthavatar.eu/. [Accessed: 10-Oct-2014].
19. 19)
  - 8. Ermes, M., Pärkkä, J., Mäntyjärvi, J., et al: ‘Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions’, IEEE Trans. Inf. Technol. Biomed., 2008, 12, (1), pp. 20–26 (doi: 10.1109/TITB.2007.899496).
20. 20)
  - 21. ‘Fitbit Flex.’ Available at: http://www.fitbit.com/uk. [Accessed: 10-Apr-2015].
21. 21)
  - 15. Minnen, D., Starner, T., Ward, J.A., et al: ‘Recognizing and discovering human actions from on-body sensor data’. IEEE Int. Conf. Multimedia and Expo, ICME, 2005, pp. 1545–1548.
22. 22)
  - 22. ‘Nike+ Fuelband.’ Available at: http://www.nike.com/gb/en_gb/c/nikeplus-fuelband. [Accessed: 10-Apr-2015].
23. 23)
  - 1. Caspersen, C.J., Powell, K.E., Christenson, G.M.: ‘Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research’, Public Health Rep., 1985, 100, (2), pp. 126–131.
24. 24)
  - 17. Qi, J., Yang, P., Fan, D., et al: ‘A survey of physical activity monitoring and assessment using internet of things technology’. 2015 IEEE Int. Conf. Computer and Information Technology; Ubiquitous Compututing Communications Dependable; Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 2353–2358.
25. 25)
  - 5. Catarinucci, L., De Donno, D., Mainetti, L., et al: ‘An IoT-aware architecture for smart healthcare systems’, IEEE Internet Things J., 2015, 4662, (c), p. 1.
26. 26)
  - 6. Yang, P., Hanneghan, M., Qi, J., et al: ‘Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment’. 2015 IEEE Int. Conf. on Computer and Information Technology; Ubiquitous Computing Communications Dependable; Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 2309–2314.
27. 27)
  - 28. Kern, N., Schiele, B., Schmidt, A.: ‘Multi-sensor activity context detection for wearable computing’, Ambient Intell., 2003, 2875, pp. 220–232 (doi: 10.1007/978-3-540-39863-9_17).
28. 28)
  - 13. Godfrey, A., Bourke, A.K., Ólaighin, G.M., et al: ‘Activity classification using a single chest mounted tri-axial accelerometer’, Med. Eng. Phys., 2011, 33, (9), pp. 1127–1135 (doi: 10.1016/j.medengphy.2011.05.002).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment

References

Related content