Recent technological advances provide researchers with a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare(compared with the whole time series) activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes.
 Atienza, A. A. and King, A. C. (2005). Comparing self-reported versus objectively measured physical activity behavior: A preliminary investigation of older Filipino American Women., Research quarterly for exercise and sport 76 358-362.
 Bai, J. (2011). Accelerometer-based prediction of activity for epidemiological research Master’s thesis, Johns Hopkins, University.
 Bao, L. and Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In, Proceedings of the 2nd International Conference on Pervasive Computing 1-17. Springer.
 Boyle, J., Karunanithi, T., Wark, T., Chan, W. and Colavitti, C. (2006). Quantifying functional mobility progress for chronic disease management In, 28th Annul Conference of the IEEE Engineering in Medicine and Biology Society 5916-5919.
 Bussmann, J. B., Martens, W. L., Tulen, J. H., Schasfoort, F. C., van den Berg-Emons, H. J. and Stam, H. J. (2001). Measuring daily behavior using ambulatory accelerometry: the activity monitor., Behavior Research Methods, Instruments, & Computers 33(3) 349-356.
 Ermes, M., Pärkka, J., Mäntyjärvi, J. and Korhonen, I. (2008). Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions., IEEE Transactions on Information Technology in Biomedicine 12 20-26.
 Feinstein, A. R., Josephy, B. R. and Wells, C. K. (1986). Scientific and clinical problems in indexes of functional disability., Annals of Internal Medicine 105 413-420.
 Freedson, P. S., Melanson, E. and Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer., Medicine & Science in Sports & Exercise 30(5) 777-781.
 Grant, P. M., Dall, P. M., Mitchell, S. L. and Granat, M. H. (2008). Activity-monitor accuracy in measuring step number and cadence in community-dwelling older adults., Journal of Aging and Physical Activity 16 204-214.
 Grant, P. M., Ryan, C. G., Tigbe, W. W. and Granat, M. H. (2006). The validation of a novel activity monitor in the measurement of posture and motion during everyday activities., British Journal of Sports Medicine 40 992-997.
 Hendelman, D., Miller, K., Baggett, C., Debold, E. and Freedson, P. (2000). Validity of accelerometry for the assessment of moderate intensity physical activity in the field., Medicine & Science in Sports & Exercise 32 442-449.
 Jelinek, F. (1997)., Statistical methods for speech recognition. the MIT Press.
 Kiani, K., Snijders, C. J. and Gelsema, E. S. (1997). Computerized analysis of daily life motor activity for ambulatory monitoring., International Journal of Technology Assessment in Health Care 5 307-318.
 Kiani, K., Snijders, C. J. and Gelsema, E. S. (1998). Recognition of daily life motor activity calsses using an artificial neural network., Archives of Physical Medicine and Rehabilitation 79 147-154.
 Kozey-Keadle, S., Libertine, A., Lyden, K., Staudenmayer, J. and FREEDSON, P. S. (2011). Validation of wearable monitors for assessing sedentary behavior., Medicine & Science in Sports & Exercise 43 1561.
 Krause, A., Sieiorek, D. P., Smailagic, A. and Farringdon, J. (2003). Unsupervised, dynamic identification of physiological and activity context in wearable computing. In, Proceedings of the 7th International Symposiu on Wearable Computers (White Plains, NY) 88-97. IEEE Computer Society.
 Mantyjarvi, J., Himberg, J. and Seppanen, T. (2001). Recognizing human motion with multiple acceleration sensors. In, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics 747-752. IEEE Press.
 McDowell, I. and Newell, C. (1987)., Measuring Health: A Guide to Rating Scales and Questionnaires. Oxford University Press, New York.
 Nguyen, A., Moore, D. and McCowan, I. (2007). Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry., 29th Annual Conference of the IEEE Engineering in Medicine and Biology Society (Lyon) 4895-4898.
 Pärkka, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J. and Korhonen, I. (2006). Activity Classification Using Realistic Data From Wearable Sensors., IEEE Transactions on Information Technology in Biomedicine 10 119–128.
 Pate, R. R., Pratt, M., Blair, S. N., Haskell, W. L., Macera, C. A., Bouchard, C., Buchner, D., Ettinger, W., Heath, G. W., King, A. C., Kriska, A., Leon, A. S., Marcus, B. H., Morris, J., Paffenbarger, R. S., Patrick, K., Pollock, M. L., Rippe, J. M., Sallis, J. and Wilmore, J. H. (1995). Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine., Journal of the American Medical Association 273 402-407.
 Picone, J. W. (2005). Signal Modeling Techniques In Speech Recognition. In, Proceedings of the IEEE 81(9) 1541-1546. IEEE Press.
 Pober, D. M., Staudenmayer, J., Raphael, C. and Freedson, P. S. (2006). Development of Novel Techniques to Classify Physical Activity Mode Using Accelerometers., Medicine & Science in Sports & Exercise 38(9) 1626-1634.
 Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., Howard, D., Meijer, K. and Crompton, R. (2009). Activity identification using body-mounted sensors—a review of classification techniques., Physiological Measurement 30 R1.
 Ravi, N., Dandekar, N., Mysore, P. and Littman, M. L. (2005). Activity recognition from accelerometer data. In, Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence 1541-1546. AAAI Press.
 Staudenmayer, J., Pober, D., Crouter, S., Bassett, D. and Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer., Journal of Applied Physiology 107(4) 338-345.
 Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T. and McDowell, M. (2008). Physical activity in the United States measured by accelerometer., Medicine & Science in Sports & Exercise 40(1) 181-188.
 Welk, G. J., Blair, S. N., Jones, S. and Thompson, R. W. (2000). A comparative evaluation of three accelerometry-based physical activity monitors., Medicine & Science in Sports & Exercise 32 489-497.
 Zhang, K., Pi-Sunyer, F. X. and Boozer, C. N. (2003). Measurement of human daily physical activity., Obesity Research 11 33-40.
 Zhang, K., Pi-Sunyer, F. X. and Boozer, C. N. (2004). Improving energy expenditure estimation for physical activity., Medicine & Science in Sports & Exercise 36 883-889.