Abstract
Typical understanding of healthcare concerns treatment, diagnosis and monitoring of diseases. But healthcare also includes well-being, healthy lifestyle, and maintaining good body condition. One of the most important factor in this respect is physical activity. Modern techniques of data acquisition and data processing enable development of advanced systems for physical activity support with use of measurement data. The need for reliable estimation routines stems from the fact, that many widely available (for bulk customers) measurements devices are not reliable and measured signals are contaminated by the noise. One of the most important variables for physical activity monitoring is the velocity of a moving object (e.g. velocity of selected parts of a body such as elbows). Apart from intensive use of system identification, optimization and control techniques for physical training support, we applied Kalman filtering technique in order to estimate speed of moving part of a body.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brzostowski, K., Drapała, J., Świątek, J.: System analysis techniques in ehealth systems: a case study. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part I. LNCS (LNAI), vol. 7196, pp. 74–85. Springer, Heidelberg (2012)
Calveert, T.W., Banister, E.W., Savage, M.V., Bach, T.: A Systems model on the Effects on Training on Physical Performance. IEEE Transaction on Systems, Man and Cybernetics SMC-6, 94–102 (1976)
Cheng, T.M., Savkin, A.V., Celler, B.G., Su, S.W., Wang, L.: Nonlinear Modeling and Control of Human Heart Rate Response During Exercise With Various Work Load Intensities. IEEE Trans. on Biomedical Engineering 55, 2499–2508 (2005)
Fitz-Clarke, J.R., Morton, R.H., Banister, E.W.: Optimizing athletic performance by influence curves. Journal of Applied Physiology 71, 1151–1158 (1991)
Greene, B.R., McGrath, D., O’Neill, R., O’Donovan, K.J., Burns, A., Caulfield, B.: An adaptive gyroscope-based algorithm for temporal gait analysis. Journal of Medical and Biological Engineering and Computing 48, 1251–1260 (2010)
Hermens, H.J., Vollenbroek-Hutten, M.M.R.: Towards remote monitoring and remotely supervised training. Journal of Electromyography and Kinesiology 18(6), 908–919 (2008)
Lim, J.E., Choi, O.H., Na, H.S., Baik, D.K.: A Context-Aware Fitness Guide System for Exercise Optimization in U-health. IEEE Trans. On Information Technology in Biomedicine 13, 370–379 (2009)
Lorincz, K., Chen, B.R., Challen, G.W.: Mercury: A Wearable Sensor Network Platform for High-Fidelity Motion Analysis. In: Proc. of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, California, pp. 183–196 (2009)
Meng, X., Zhang, Z.Q., Wu, J.K., Wong, W.C.: Hierarchical Information Fusion for Global Displacement Estimation in Microsensor Motion Capture. IEEE Transaction on Biomedical Engineering 60, 2052–2063 (2013)
Moxnes, J.F., Hausken, K.: The dynamics of athletic performance, fitness and fatigue. Mathematical and Computer Modelling of Dynamical Systems 14, 515–533 (2008)
Pantaelopoulos, A., Bourbakis, N.G.: A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 1–12 (2010)
Smołka, ł., Ochmann, B.: A Novel Method of Anaerobic Performance Assessment in Swimming. Journal of Strength and Conditioning Research 27, 533–539 (2013)
adidas miCoach: The Interactive Personal Coaching and Training System (2011), http://www.micoach.com (accessed: September 14, 2013)
Polar, http://www.polar.fi (accessed: September 15, 2014)
Porta, J.P., Acosta, D.J., Lehker, A.N., Miller, S.T., Tomaka, J., King, G.A.: Validating the Adidas miCoach for estimating pace, distance, and energy expenditure during outdoor over-ground exercise accelerometer. International Journal of Exercise Science 2 (2012)
Simon, D.: Optimal State Estimation: Kalman, H inf and Nonlinear Approaches. John Wiley & Sons (2006)
Suunto, http://www.suunto.com (accessed: September 14, 2014)
Shimmer Sensing, http://www.shimmersensing.com (accessed: September 14, 2014)
Stalker, http://www.stalkerradar.com (accessed: September 14, 2014)
Świątek, J.: Some problems of complex static systems identification. Wrocław University of Technology Publishing House, Poland (2009)
Świątek, P., Klukowski, P., Brzostowski, K., Drapała, J.: Application of Wearable Smart System to Support Physical Activity. In: Frontiers in Artificial Intelligence and Applications. Advances in Knowledge-Based and Intelligent Information and Engineering Systems, vol. 243, pp. 1418–1427 (2012)
Twomey, N., Faul, S., Marnane, W.P.: Comparison of accelerometer-based energy expenditure estimation algorithms. In: Proc. of the 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1–8 (2010)
Vales-Alonso, J., et al.: Ambient Intelligence Systems for Personalized Sport Training. Sensors 10(3), 2359–2385 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Brzostowski, K., Drapała, J., Świątek, J. (2014). Application of Nonlinear State Estimation Methods for Sport Training Support. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-05476-6_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
eBook Packages: Computer ScienceComputer Science (R0)