Energy expenditure estimation with smartphone body sensors

Amit Pande, Yunze Zeng, Aveek K. Das, Prasant Mohapatra, Sheridan Miyamoto, Edmund Seto, Erik K Henricson, Jay J. Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


Energy Expenditure Estimation (EEE) is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EEE utilizing small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EEE for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, we build a generic regression model for EEE that yields upto 89% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 15%). We compare our results against stateof- The-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). We were able to demonstrate the superior accuracy achieved by our algorithm. The results were calibrated against COSMED K4b2 calorimeter readings.

Original languageEnglish (US)
Title of host publicationBODYNETS 2013 - 8th International Conference on Body Area Networks
Number of pages7
ISBN (Print)9781936968893
StatePublished - Oct 29 2013
Event8th International Conference on Body Area Networks, BODYNETS 2013 - Boston, United States
Duration: Sep 30 2013Oct 2 2013


Other8th International Conference on Body Area Networks, BODYNETS 2013
Country/TerritoryUnited States


  • Accelerometer
  • Artificial neural networks
  • Barometer
  • Energy expenditure

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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