Abstract
Monitoring of posture allocations and activities is important for such applications as physical activity management, energy expenditure estimation, stroke rehabilitation and others. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This chapter presents an overview of a novel wearable footwear sensor (SmartShoe), which is capable of very accurate recognition of most common postures and activities while being minimally intrusive to the subject. SmartShoe relies on capturing information from patterns of heel acceleration and plantar pressure to differentiate weight-bearing and non-weight-bearing activities (such as for example, sitting and standing, walking/jogging and cycling). Validation results obtained in several studies demonstrate applicability to widely varying populations such as healthy individuals and individuals post-stroke, while achieving high (95%-98%) average accuracy of posture and activity classification, high (root-mean-square error of 0.69 METs) accuracy of energy expenditure prediction, and reliable (error of 2.6- 18.6%) identification of temporal gait parameters. High accuracy and minimal intrusiveness of SmartShoe should enable its use in a wide range of research and clinical applications.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Flegal, K.M., Carroll, M.D., Kit, B.K., Ogden, C.L.: Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010. JAMA 307(5), 491–497 (2012)
Hill, J.O., Wyatt, H.R., Reed, G.W., Peters, J.C.: Obesity and the Environ-ment: Where Do We Go from Here? Science 299(5608), 853–855 (2003)
Levine, J.A., Lanningham-Foster, L.M., McCrady, S.K., Krizan, A.C., Ol-son, L.R., Kane, P.H., Jensen, M.D., Clark, M.M.: Interindividual variation in posture allocation: possible role in human obesity. Science 307(5709), 584–586 (2005)
Shaughnessy, M., Michael, K.M., Sorkin, J.D., Macko, R.F.: Steps After Stroke Capturing Ambulatory Recovery. Stroke 36(6), 1305–1307 (2005)
Fulk, G.D., Reynolds, C., Mondal, S., Deutsch, J.E.: Predicting home and community walking activity in people with stroke. Arch. Phys. Med. Rehabil. 91(10), 1582–1586 (2010)
Levin, M.F., Kleim, J.A., Wolf, S.L.: What Do Motor ‘Recovery’ and ‘Compensation’ Mean in Patients Following Stroke? Neurorehabil. Neural Repair 23(4), 313–319 (2009)
Bonomi, A.G., Plasqui, G., Goris, A.H.C., 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 (2009)
Brage, S., Brage, N., Franks, P.W., Ekelund, U., Wong, M.-Y., Andersen, L.B., Froberg, K., Wareham, N.J.: Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J. Appl. Physiol. 96(1), 343–351 (2004)
Bamberg, S.J.M., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E., Paradiso, J.A.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–423 (2008)
de Niet, M., Bussmann, J.B., Ribbers, G.M., Stam, H.J.: The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch. Phys. Med. Rehabil. 88(9), 1121–1126 (2007)
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)
Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 10(1), 119–128 (2006)
Ermes, M., Pärkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 12(1), 20–26 (2008)
Lester, J., Choudhury, T., Borriello, G.: A Practical Approach to Recognizing Physical Activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)
Havinga, P.J.M., Marin-Perianu, M., Thalen, J.P.: SensorShoe: Mobile Gait Analysis for Parkinson’s Disease Patients (2007)
Jagos, H., Oberzaucher, J.: Development of a Wearable Measurement System to Identify Characteristics in Human Gait - eSHOE -. In: Miesenberger, K., Klaus, J., Zagler, W.L., Karshmer, A.I. (eds.) ICCHP 2008. LNCS, vol. 5105, pp. 1301–1304. Springer, Heidelberg (2008)
Zhang, K., Pi-Sunyer, F.X., Boozer, C.N.: Improving energy expenditure estimation for physical activity. Med. Sci. Sports Exerc. 36(5), 883–889 (2004)
Tharion, W.J., Yokota, M., Buller, M.J., DeLany, J.P., Hoyt, R.W.: Total energy expenditure estimated using a foot-contact pedometer. Med. Sci. Monit. 10(9), CR504–CR509 (2004)
Sazonova, N., Browning, R., Sazonov, E.: Accurate Prediction of Energy Expenditure Using a Shoe-Based Activity Monitor. Medicine & Science in Sports & Exercise 43(7), 1312–1321 (2011)
Fulk, G., Sazonov, E.: Using Sensors to Measure Activity in People with Stroke. Topics in Stroke Rehabilitation 18(6), 746–757 (2011)
Edgar, S.R., Swyka, T., Fulk, G., Sazonov, E.S.: Wearable shoe-based device for rehabilitation of stroke patients. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3772–3775 (2010)
Sazonov, E.S., Fulk, G., Hill, J., Schutz, Y., Browning, R.: Monitoring of Posture Allocations and Activities by a Shoe-Based Wearable Sensor. IEEE Transactions on Biomedical Engineering 58(4), 983–990 (2011)
Brockway, J.M.: Derivation of formulae used to calculate energy expenditure in man. Hum. Nutr. Clin. Nutr. 41(6), 463–471 (1987)
Sazonov, E., Krishnamurthy, V., Schilling, R.: Wireless Intelligent Sensor and Actuator Network - A Scalable Platform for Time-Synchronous Applications of Structural Health Monitoring. Structural Health Monitoring (April 2010)
Chih-Chung, C., Chih-Jen, L.: LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. In: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, pp. 113–116 (2006)
Saremi, K., Marehbian, J., Yan, X., Regnaux, J.-P., Elashoff, R., Bussel, B., Dobkin, B.H.: Reliability and Validity of Bilateral Thigh and Foot Accelerometry Measures of Walking in Healthy and Hemiparetic Subjects. Neurorehabil. Neural Repair 20(2), 297–305 (2006)
Lau, H., Tong, K., Zhu, H.: Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Human Movement Science 28(4), 504–514 (2009)
Aminian, K., Najafi, B., Büla, C., Leyvraz, P.-F., Robert, P.: Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of Biomechanics 35(5), 689–699 (2002)
Staudenmayer, J., Pober, D., Crouter, S.E., Bassett, D.R., Freedson, P.: An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J. Appl. Physiol., 00465 (2009)
Crouter, S.E., Clowers, K.G., Bassett, D.R.: A novel method for using accelerometer data to predict energy expenditure. J. Appl. Physiol. 100(4), 1324–1331 (2006)
Choi, L., Chen, K.Y., Acra, S.A., Buchowski, M.S.: Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth. J. Appl. Physiol. 108(2), 314–327 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sazonov, E. (2013). Footwear-Based Wearable Sensors for Physical Activity Monitoring. In: Mukhopadhyay, S., Postolache, O. (eds) Pervasive and Mobile Sensing and Computing for Healthcare. Smart Sensors, Measurement and Instrumentation, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32538-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-32538-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32537-3
Online ISBN: 978-3-642-32538-0
eBook Packages: EngineeringEngineering (R0)