Using wearable activity type detection to improve physical activity energy expenditure estimation

Authors:
Fahd Albinali;Stephen Intille;William Haskell;Mary Rosenberger
Affiliations:
Massachusetts Institute of Technology, Cambridge, MA, USA;Massachusetts Institute of Technology, Cambridge, MA, USA;Stanford Medical School, Stanford, CA, USA;Stanford Medical School, Stanford, CA, USA
Venue:
Proceedings of the 12th ACM international conference on Ubiquitous computing
Year:
2010

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Abstract

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.