Abstract
Physical activity recognition represents a new frontier of improvement for context-aware applications, and for several other applications related to public health. Activity recognition requires the monitoring of physical activity in unconfined environments, using automatic systems supporting prolonged observation periods, and providing minimal discomfort to the user. Accelerometers reasonably satisfy these requirements and have therefore often been employed to identify physical activity types. This chapter will describe how the different applications of activity recognition would influence the choice of the on-body placement and the number of accelerometers. After that it will be analyzed which sampling frequency is necessary to record an acceleration signal for the purpose of activity pattern recognition, and which is the optimal strategy to segment the recorded signal to improve the recognition performance in daily life. In conclusion, it will be discussed how the user friendliness of accelerometers is influenced by the classification algorithm and by the data processing required for activity recognition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Antonsson, E.K., Mann, R.W.: The frequency content of gait. J. Biomech. 18, 39–47 (1985)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. Pervasive Comput. Proc 3001, 1–17 (2004)
Boissy, P., Choquette, S., Hamel, M., Noury, N.: User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemed. J. E. Health 13, 683–693 (2007)
Bonomi, A.G., Plasqui, G., Goris, A.H., Westerterp, K.R.: Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J. Appl. Physiol. 107(3), 655–661 (Sept. 2009a). Epub 25 June 2009
Bonomi, A.G., Goris, A.H., Yin, B., Westerterp, K.R.: Detection of type, duration, and intensity of physical activity using an accelerometer. Med. Sci. Sports Exerc. 41(9), 1770–1777 (Sept. 2009b)
Brage, S., Wedderkopp, N., Ekelund, U., Franks, P.W., Wareham, N.J., Andersen, L.B., Froberg, K.: Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care 27, 2141–2148 (2004)
Crouter, S.E., Churilla, J.R., Bassett, D.R., Jr.: Estimating energy expenditure using accelerometers. Eur. J. Appl. Physiol. 98, 601–612 (2006)
Ekelund, U., Brage, S., Besson, H., Sharp, S., Wareham, N.J.: Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality? Am. J. Clin. Nutr. 88, 612–617 (2008)
Ermes, M., Parkka, J., Cluitmans, L.: Advancing from offline to online activity recognition with wearable sensors. Engineering in medicine and biology society: 30th annual international conference of the IEEE, pp. 4451–4454 (2008a)
Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inform. Technol. Biomed. 12, 20–26 (2008b)
Gurley, R.J., Lum, N., Sande, M., Lo, B., Katz, M.H.: Persons found in their homes helpless or dead. N. E. J. Med. 334, 1710–1716 (1996)
Harris, T.J., Owen, C.G., Victor, C.R., Adams, R., Ekelund, U., Cook, D.G.: A comparison of questionnaire, accelerometer, and pedometer: measures in older people. Med. Sci. Sports Exerc. 41, 1392–1402 (2009)
Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., et al.: 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, 1423–1434 (2007)
Hu, F.B., Li, T.Y., Colditz, G.A., Willett, W.C., Manson, J.E.: Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 289, 1785–1791 (2003)
Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10, 156–167 (2006)
Mathie, M.J., Coster, A.C., Lovell, N.H., Celler, B.G.: Detection of daily physical activities using a triaxial accelerometer. Med. Biolog. Eng. Comput. 41, 296–301 (2003)
Melanson, E.L., Jr., Freedson, P.S.: Physical activity assessment: a review of methods. Crit. Rev. Food Sci. Nutr. 36, 385–396 (1996)
Plasqui, G., Westerterp, K.R.: Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity 15, 2371–2379 (2007)
Pober, D.M., Staudenmayer, J., Raphael, C., Freedson, P.S.: Development of novel techniques to classify physical activity mode using accelerometers. Med. Sci. Sports Exerc. 38, 1626–1634 (2006)
Preece, S.J., Goulermas, J.Y., Kenney, L.P., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors–a review of classification techniques. Physiol. Meas. 30(4), R1–R33 (2009 Apr.). Epub 2 Apr. 2009
Shannon, C.E.: Communication in the presence of noise. Proc. Inst. Radio Eng. 37(1), 10–21 (1949)
Veltink, P.H., Bussmann, H.B., de Vries, W., Martens, W.L., Van Lummel, R.C.: Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehabil. Eng. 4, 375–385 (1996)
Wild, D., Nayak, U.S., Isaacs, B.: How dangerous are falls in old people at home? Br. Med. J. (Clin. Res. Ed) 282, 266–268 (1981)
Zhang, K., Werner, P., Sun, M., Pi-Sunyer, F.X., Boozer, C.N.: Measurement of human daily physical activity. Obes. Res. 11, 33–40 (2003)
Acknowledgement
The author thanks Chen Xin for recruiting the study participants and for following the experimental protocol of the study aimed at determining the optimal sampling frequency of accelerometers for activity recognition.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Bonomi, A.G. (2010). Physical Activity Recognition Using a Wearable Accelerometer. In: Westerink, J., Krans, M., Ouwerkerk, M. (eds) Sensing Emotions. Philips Research Book Series, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3258-4_3
Download citation
DOI: https://doi.org/10.1007/978-90-481-3258-4_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3257-7
Online ISBN: 978-90-481-3258-4
eBook Packages: Computer ScienceComputer Science (R0)