Smart Health pp 99-124 | Cite as

Personalized Physical Activity Monitoring Using Wearable Sensors

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8700)


It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.


Physical activity monitoring ADL Strength exercises Personalization Wearable sensors Inertial sensors HCI Ambient assisted living 


  1. 1.
    Ainsworth, B.E., Haskell, W.L., Whitt, M.C., Irwin, M.L., Swartz, A.M., Strath, S.J., O’Brien, W.L., Bassett, D.R., Schmitz, K.H., Emplaincourt, P.O., Jacobs, D.R., Leon, A.S.: Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc. 32(9), 498–516 (2000)CrossRefGoogle Scholar
  2. 2.
    Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recogn. 43(10), 3605–3620 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  4. 4.
    Berger, K.: The Developing Person: Through the Life Span. Worth Publishers, New York (2008)Google Scholar
  5. 5.
    Bishop, C.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)zbMATHGoogle Scholar
  6. 6.
    Bleser, G., Steffen, D., Weber, M., Hendeby, G., Stricker, D., Fradet, L., Marin, F., Ville, N., Carré, F.: A personalized exercise trainer for the elderly. J. Ambient Intell. Smart Environ. 5, 547–562 (2013)Google Scholar
  7. 7.
    Bloit, J., Rodet, X.: Short-time Viterbi for online HMM decoding: evaluation on a real-time phone recognition task. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2121–2124 (2008)Google Scholar
  8. 8.
    Costa, C., Tacconi, D., Tomasi, R., Calva, F., Terreri, V.: RIABLO: a game system for supporting orthopedic rehabilitation. In: Conference of the Italian SIGCHI Chapter (CHItaly 2013), September 2013Google Scholar
  9. 9.
    Dick, F.W.: Sports Training Principles. A. & C. Black, London (1997)Google Scholar
  10. 10.
    El-Gohary, M., McNames, J.: Shoulder and elbow joint angle tracking with inertial sensors. IEEE Trans. Biomed. Eng. 59(9), 2635–2641 (2012)CrossRefGoogle Scholar
  11. 11.
    Ermes, M., Pärkkä, J., Cluitmans, L.: Advancing from offline to online activity recognition with wearable sensors. In: Proceedings of 30th Annual International IEEE EMBS Conference, Vancouver, Canada, pp. 4451–4454, August 2008Google Scholar
  12. 12.
    Fisk, A.D., Rogers, W.A., Charness, N., Czaja, S.J., Sharit, J.: Designing for Older Adults: Principles and Creative Human Factors Approaches. CRC Press, Boca Raton (2009)CrossRefGoogle Scholar
  13. 13.
    Haskell, W.L., Lee, I.-M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., Macera, C.A., Heath, G.W., Thompson, P.D., Bauman, A.: Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med. Sci. Sports Exerc. 39(8), 34–1423 (2007)CrossRefGoogle Scholar
  14. 14.
    Hocoma. VALEDO\(\textregistered \)MOTION. Accessed June 2014
  15. 15.
    Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings of Joint Conference on Smart Objects and Ambient Intelligence (sOc-EuSAI), pp. 159–163 (2005)Google Scholar
  16. 16.
    Kirkendall, D.: Exercise prescription for the healthy adult. Prim. Care 11(1), 23–31 (1984)Google Scholar
  17. 17.
    Ko, M.H., West, G., Venkatesh, S., Kumar, M.: Using dynamic time warping for online temporal fusion in multisensor systems. Inf. Fusion 9(3), 370–388 (2008)CrossRefGoogle Scholar
  18. 18.
    Long, X., Yin, B., Aarts, R.M.: Single-accelerometer based daily physical activity classification. In: Proceedings of 31st Annual International IEEE EMBS Conference, Minneapolis, MN, USA, pp. 6107–6110, September 2009Google Scholar
  19. 19.
    Maekawa, T., Watanabe, S.: Unsupervised activity recognition with user’s physical characteristics data. In: Proceedings of IEEE 15th International Symposium on Wearable Computers (ISWC), San Francisco, CA, USA, pp. 89–96, June 2011Google Scholar
  20. 20.
    Mazzeo, R., Tanaka, H.: Exercise prescription for the elderly: current recommendations. Sports Med. 31, 809–818 (2001)CrossRefGoogle Scholar
  21. 21.
    Miezal, M., Bleser, G., Schmitz, N., Stricker, D.: A generic approach to inertial tracking of arbitrary kinematic chains. In: International Conference on Body Area Networks, Bosten, US, September 2013Google Scholar
  22. 22.
    Minnen, D., Isbell, C., Essa, I., Starner, T.: Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. In: Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI), vol. 1, pp. 615–620 (2007)Google Scholar
  23. 23.
    Pärkkä, J., Cluitmans, L., Ermes, M.: Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree. IEEE Trans. Inf. Technol. Biomed. 14(5), 1211–1215 (2010)CrossRefGoogle Scholar
  24. 24.
    Patel, S., Mancinelli, C., Healey, J., Moy, M., Bonato, P.: Using wearable sensors to monitor physical activities of patients with COPD: a comparison of classifier performance. In: Proceedings of 6th International Workshop on Wearable and Implantable Body Sensor Networks (BSN), Berkeley, CA, USA, pp. 234–239, June 2009Google Scholar
  25. 25.
    Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(21), 1–17 (2012)Google Scholar
  26. 26.
    Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  27. 27.
    Reiss, A.: PAMAP2 Physical Activity Monitoring Data Set., 22 November 2013
  28. 28.
    Reiss, A., Hendeby, G., Bleser, G., Stricker, D.: Activity recognition using biomechanical model based pose estimation. In: Lukowicz, P., Kunze, K., Kortuem, G. (eds.) EuroSSC 2010. LNCS, vol. 6446, pp. 42–55. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  29. 29.
    Reiss, A., Hendeby, G., Stricker, D.: Confidence-based multiclass AdaBoost for physical activity monitoring. In: Proceedings of IEEE 17th International Symposium on Wearable Computers (ISWC), Zurich, Switzerland, September 2013Google Scholar
  30. 30.
    Reiss, A., Hendeby, G., Stricker, D.: Towards robust activity recognition for everyday life: methods and evaluation. In: Proceedings of 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Venice, Italy, May 2013Google Scholar
  31. 31.
    Reiss, A., Stricker, D.: Introducing a modular activity monitoring system. In: Proceedings of 33rd Annual International IEEE EMBS Conference, Boston, MA, USA, pp. 5621–5624, August–September 2011Google Scholar
  32. 32.
    Reiss, A., Stricker, D.: Creating and benchmarking a new dataset for physical activity monitoring. In: Proceedings of 5th Workshop on Affect and Behaviour Related Assistance (ABRA), Crete, Greece, June 2012Google Scholar
  33. 33.
    Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: Proceedings of IEEE 16th International Symposium on Wearable Computers (ISWC), Newcastle, UK, pp. 108–109, June 2012Google Scholar
  34. 34.
    Reiss, A., Stricker, D.: Personalized mobile physical activity recognition. In: Proceedings of IEEE 17th International Symposium on Wearable Computers (ISWC), Zurich, Switzerland, September 2013Google Scholar
  35. 35.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence. Prentice Hall, Englewood Cliffs (2010)Google Scholar
  36. 36.
    Salehi, S., Bleser, G., Schmitz, N., Stricker, D.: A low-cost and light-weight motion tracking suit. In: IEEE International Conference on Ubiquitous Intelligence and Computing, Vietri sul Mare, Italy, December 2013Google Scholar
  37. 37.
    Sears, A., Jacko, J.A.: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications. CRC Press, Baco Raton (2007)CrossRefGoogle Scholar
  38. 38.
    Taylor, M., McCormick, D., Impson, R., Shawis, T., Griffin, M.: Activity promoting gaming systems in exercise and rehabilitation. J. Rehabil. Res. Dev. 48, 1171–1186 (2011)CrossRefGoogle Scholar
  39. 39.
    Trivisio. Colibri Wireless - Inertial Motion Tracker. Last Accessed June 2014
  40. 40.
    Trivisio. MotionVizard. Accessed June 2014
  41. 41.
    Warburton, D., Nicol, C., Bredin, S.: Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174(6), 801–809 (2006)CrossRefGoogle Scholar
  42. 42.
    Weber, M., Bleser, G., Hendeby, G., Reiss, A., Stricker, D.: Unsupervised model generation for motion monitoring. In: IEEE International Conference on Systems, Man and Cybernetics - Workshop on Robust Machine Learning Techniques for Human Activity Recognition, Anchorage, pp. 51–54. IEEE (2011)Google Scholar
  43. 43.
    Weber, M., Liwicki, M., Bleser, G., Stricker, D.: Unsupervised motion pattern learning for motion segmentation. In: International Conference on Pattern Recognition (ICPR), Tsukuba Science City, Japan (2012)Google Scholar
  44. 44.
    Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)CrossRefMathSciNetGoogle Scholar
  45. 45.
    Winnett, R.A., Carpinelli, R.N.: Potential health-related benefits of resistance training. Prev. Med. 33, 503–513 (2001)CrossRefGoogle Scholar
  46. 46.
    Xsens. MTx. Accessed June 2014
  47. 47.
    Xsens. MVN - Inertial Motion Capture. Accessed June 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.German Research Center for Artificial IntelligenceKaiserslauternGermany
  2. 2.ACTLabUniversity of PassauPassauGermany
  3. 3.Department of Electrical EngineeringLinköping UniversityLinköpingSweden
  4. 4.Department of Sensor and EW SystemsSwedish Defence Research Agency (FOI)LinköpingSweden
  5. 5.Université de PoitiersPoitiersFrance

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