Name
Abstract | ||
|---|---|---|
Accurate and online Energy Expenditure Estimation (EEE) utilizing small wearable sensors is a difficult task with most existing schemes. In this work, we focus on accurate EEE for tracking ambulatory activities 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, a generic regression model for EEE is built that yields upto 83% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2534088.2534099 | Wireless Health 2013 |
Keywords | Field | DocType |
actual energy expenditure,online energy expenditure estimation,common smartphone user,existing scheme,barometer sensor,accurate eee,eee performance,accurate energy expenditure estimation,smartphone sensor,barometer data,yields upto,artificial neural networks,energy expenditure,barometer | Nike,Wearable computer,Accelerometer,Simulation,Energy expenditure,Electronics,Barometer,Engineering,Artificial neural network | Conference |
Citations | PageRank | References |
5 | 0.64 | 5 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amit Pande | 1 | 269 | 24.58 |
| Yunze Zeng | 2 | 151 | 9.45 |
| Aveek Das | 3 | 13 | 2.31 |
| Prasant Mohapatra | 4 | 4344 | 304.46 |
| Sheridan Miyamoto | 5 | 17 | 1.69 |
| Edmund Seto | 6 | 194 | 16.52 |
| Erik K. Henricson | 7 | 17 | 1.69 |
| Jay J. Han | 8 | 26 | 4.09 |