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Using Physical Activity Monitors in Smart Environments and Social Networks: Applications and Challenges

  • Jose-Luis Sanchez-RomeroEmail author
  • Antonio Jimeno-Morenilla
  • Higinio Mora
  • Francisco Pujol-Lopez
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

The use of smart watches and fitness wrists has been increasing in recent years. On the one hand, their cost has become cheaper and their performance has improved. On the other hand, the increase in the number of people who practice sports such as running and cycling is another factor to consider. The increase in the number of popular athletes has meant that these devices are no longer considered intended for a minority or an elite, but are even used in people’s daily movements or in simpler activities such as walking. Some of these devices simply count the number of steps taken, while more advanced devices include energy consumption, distance travelled, speed, GPS tracking position, altimetry or heart rate. Moreover, there are social networks that allow athletes to share information gathered by their own activities, especially track and altimetry. This opens up a wider range of possibilities in sports training. However, the gathering of this rich information makes many more applications possible. For example, city planners can analyze the movements of people to detect possible shortcomings in public transport systems, deficiencies in urban pathways, and so on. This research work shows a taxonomy of different applications that use the data gathered by physical activity monitors and shared in social networks. The opportunities and drawbacks regarding the use of such applications in intelligent environments are also discussed.

References

  1. 1.
    Mercer, K., et al.: Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth uHealth 4(1) (2016)CrossRefGoogle Scholar
  2. 2.
    Guillen-Garcia, F., Castro-Sanchez, J.J., Guillen-Garcia, M.A.: Calidad de vida, salud y ejercicio fisico: una aproximacion al tema desde una perspectiva psicosocial. Rev. Psicol. deporte 6(2), 0091–110 (1997)Google Scholar
  3. 3.
    Franco, L., et al.: Effectiveness of a customised, unsupervised 4 month exercise programme on exercise tolerance, perception of fatigue and anthropometric variables in sedentary patients with cardiovascular risk factors. Arch. Med. Dep. 33(175), 325–330 (2016)Google Scholar
  4. 4.
    Miguel-Soca, P.E.: The metabolic syndrome: a high risk for sedentary persons. Rev. Cub. Inf. Ciencias Salud (ACIMED) 20(2), 1–8 (2009)Google Scholar
  5. 5.
    Warburton, D.E., Nicol, C.W., Bredin, S.S.: Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174(6), 801–809 (2006)CrossRefGoogle Scholar
  6. 6.
    Behrendt, F.: Why cycling matters for smart cities. Internet of bicycles for intelligent transport. J. Transp. Geogr. 56, 157–164 (2016)CrossRefGoogle Scholar
  7. 7.
    Rhodes, B.J., Maes, P.: Just-in-time information retrieval agents. IBM Syst. J. 39(3.4), 685–704 (2000)CrossRefGoogle Scholar
  8. 8.
    Lymberis, A.: Smart wearable systems for personalised health management: current R&D and future challenges. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, New York, pp. 3716–3719 (2003)Google Scholar
  9. 9.
    Starner, T.: The challenges of wearable computing: Part 1. IEEE Micro 21(4), 44–52 (2001)CrossRefGoogle Scholar
  10. 10.
    Yetisen, A.K., et al. Wearables in medicine. Adv. Mater. 1706910 (2018)Google Scholar
  11. 11.
    Rawassizadeh, R., Price, B.A., Petre, M.: Wearables: has the age of smartwatches finally arrived? Commun. ACM 58(1), 45–47 (2015)CrossRefGoogle Scholar
  12. 12.
    Lupton, D.: Self-tracking, health and medicine. Health Sociol. Rev. 26(1), 1–5 (2017)CrossRefGoogle Scholar
  13. 13.
    Piwek, L., et al.: The use of self-monitoring solutions amongst cyclists: an online survey and empirical study. Transp. Res. Part A Policy Pract. 77, 126–136 (2015)CrossRefGoogle Scholar
  14. 14.
    Tomberg, V., Schulz, T., Kelle, S.: Applying universal design principles to themes for wearables. In: International Conference on Universal Access in Human-Computer Interaction, pp. 550–560. Springer, Berlin (2015)CrossRefGoogle Scholar
  15. 15.
    Piwek, L., et al.: The rise of consumer health wearables: promises and barriers. PLoS Med. 13(2), e1001953 (2016)CrossRefGoogle Scholar
  16. 16.
    Kamišalić, A., et al.: Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors 18(6), 1714 (2018)CrossRefGoogle Scholar
  17. 17.
    Lupton, D.: Lively Data, social fitness and biovalue: the intersections of health self-tracking and social media (September 27, 2015). In: Burgess, J., Marwick, A., Poell, T. (eds.) The sage handbook of social media. Sage, London (2017)Google Scholar
  18. 18.
    Kranz, M., et al.: The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive Mob. Comput. 9(2), 203–215 (2013)CrossRefGoogle Scholar
  19. 19.
    Lytras, M.D., Visvizi, A., Daniela, L., Sarirete, A., Ordonez De Pablos, P.: Social networks research for sustainable smart education. Sustainability 10(9), 2974 (2018)CrossRefGoogle Scholar
  20. 20.
    Cortes, R., Bonnaire, X., Marin, O., Sens, P.: Sport trackers and big data: Studying user traces to identify opportunities and challenges. Doctoral dissertation, INRIA Paris (2014)Google Scholar
  21. 21.
    Griffin, G.P., Jiao, J.: Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus. J. Transp. Health 2(2), 238–247 (2015)CrossRefGoogle Scholar
  22. 22.
    Sun, Y., et al.: Examining associations of environmental characteristics with recreational cycling behaviour by street-level Strava data. Int. J. Environ. Res. Public Health 14(6), 644 (2017)CrossRefGoogle Scholar
  23. 23.
    Sun, Y.: Exploring potential of crowdsourced geographic information in studies of active travel and health: Strava data and cycling behaviour. In: ISPRS Geospatial Week 2017, Wuhan, China, 18–22 Sept 2017, pp. 1357–1361 (2017)CrossRefGoogle Scholar
  24. 24.
    Norman, G., Kesha, N.: Using smartphones for cycle planning. In: IPENZ Transportation Group Conference, pp. 22–24 (2015)Google Scholar
  25. 25.
    Sun, Y., Mobasheri, A.: Utilizing crowdsourced data for studies of cycling and air pollution exposure: a case study using Strava Data. Int. J. Environ. Res. Public Health 14(3), 274 (2017)CrossRefGoogle Scholar
  26. 26.
    Sun, Y., et al.: Investigating impacts of environmental factors on the cycling behavior of bicycle-sharing users. Sustainability 9(6), 1060 (2017)CrossRefGoogle Scholar
  27. 27.
    Musakwa, W., Selala, K.M.: Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa. Data Brief 9, 898–905 (2016)CrossRefGoogle Scholar
  28. 28.
    Lemmer, J.T., et al.: Age and gender responses to strength training and detraining. Med. Sci. Sports Exerc. 32(8), 1505–1512 (2000)CrossRefGoogle Scholar
  29. 29.
    Balderrama, C., et al.: Evaluation of three methodologies to estimate the VO2max in people of different ages. Applied ergonomics 42(1), 162–168 (2010)CrossRefGoogle Scholar
  30. 30.
    Munos, B., et al.: Mobile health: the power of wearables, sensors, and apps to transform clinical trials. Ann. N. Y. Acad. Sci. 1375(1), 3–18 (2016)CrossRefGoogle Scholar
  31. 31.
    Amft, O.: How wearable computing is shaping digital health. IEEE Pervasive Comput. 1, 92–98 (2018)CrossRefGoogle Scholar
  32. 32.
    Cintia, P., Pappalardo, L., Pedreschi, D.: Engine matters: a first large scale data driven study on cyclists’ performance. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 147–153 (2013)Google Scholar
  33. 33.
    Lytras, M.D., Visvizi, A.: Who uses smart city services and what to make of it: toward interdisciplinary smart cities research. Sustainability 10(6), 1998 (2018)CrossRefGoogle Scholar
  34. 34.
    Mora, H., Perez-DelHoyo, R., Paredes-Perez, J.F., Molla-Sirvent, R.A.: Analysis of social networking service data for smart urban planning. Sustainability 10, 4732 (2018)CrossRefGoogle Scholar
  35. 35.
    Stamatiadis, N., Pappalardo, G., Cafiso, S.: Use of technology to improve bicycle mobility in smart cities. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, Bew York, pp. 86–91 (2017)Google Scholar
  36. 36.
    Spil, T., et al.: The adoption of wearables for a healthy lifestyle: can gamification help? In: Proceedings of the 50th Hawaii International Conference on Systems Sciences, pp. 3617–3625 (2017)Google Scholar
  37. 37.
    Derlyatka, A., et al.: Bright spots, physical activity investments that work: Sweatcoin: a steps generated virtual currency for sustained physical activity behaviour change. Br. J. Sports Med., p. bjsports-2018-099739 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jose-Luis Sanchez-Romero
    • 1
    Email author
  • Antonio Jimeno-Morenilla
    • 1
  • Higinio Mora
    • 1
  • Francisco Pujol-Lopez
    • 1
  1. 1.University of AlicanteSan Vicente del RaspeigSpain

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