F. Albinali, S. S. Intille, W. Haskell, and M. Rosenberger, "Using wearable activity type detection to improve physical activity energy expenditure estimation," in Proceedings of the 12th International Conference on Ubiquitous Computing, New York: ACM Press, pp. 311-320, 2010. [PDF]


Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.


We thank Jeff Myll and Selene Mota for assistance with data collection and processing. This work was funded by the National Institutes of Health grant #5U01HL091737. The development of some sensors was funded by National Science Foundation grant #0313065.