Name
Abstract | ||
|---|---|---|
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples). |
Year | Venue | Field |
|---|---|---|
2018 | Knowledge Discovery and Data Mining | Wrist,Wearable computer,Simulation,Accelerometer,Photoplethysmogram,Mean absolute error,Acceleration,Heart rate,Statistics,Wearable technology,Mathematics |
DocType | Volume | Citations |
Journal | abs/1807.04667 | 0 |
PageRank | References | Authors |
0.34 | 4 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan McConville | 1 | 0 | 1.01 |
| Gareth Archer | 2 | 0 | 0.34 |
| Ian Craddock | 3 | 137 | 15.93 |
| Herman J. Ter Horst | 4 | 32 | 5.96 |
| Robert J. Piechocki | 5 | 333 | 47.70 |
| James Pope | 6 | 17 | 4.23 |
| Raúl Santos-Rodríguez | 7 | 36 | 12.41 |