Name
Title | ||
|---|---|---|
Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals. |
Abstract | ||
|---|---|---|
Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise. However, high accuracy and low computational complexity are conflicting requirements. We propose a method that provides highly accurate heart rate estimates at a very low computational cost in order to be implementable on wearables. To achieve the lowest possible complexity, only basic signal processing operations, i.e., correlation-based fundamental frequency estimation and spectral combination, harmonic noise damping and frequency domain tracking, are used. The proposed approach outperforms state-of-the-art methods on current benchmark data considerably in terms of computation time, while achieving a similar accuracy. |
Year | Venue | Keywords |
|---|---|---|
2017 | European Signal Processing Conference | Photoplethysmography (PPG),Heart Rate Estimation,Motion Artifacts (MA) |
Field | DocType | ISSN |
Frequency domain,Signal processing,Fundamental frequency,Wearable computer,Computer science,Harmonic,Real-time computing,Wearable technology,Computation,Computational complexity theory | Conference | 2076-1465 |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Schäck | 1 | 3 | 1.77 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |