Name
Title | ||
|---|---|---|
Unobtrusive heart rate estimation during physical exercise using photoplethysmographic and acceleration data |
Abstract | ||
|---|---|---|
Photoplethysmography (PPG) is a non-invasive, inexpensive and unobtrusive method to achieve heart rate monitoring during physical exercises. Motion artifacts during exercise challenge the heart rate estimation from wrist-type PPG signals. This paper presents a methodology to overcome these limitation by incorporating acceleration information. The proposed algorithm consisted of four stages: (1) A wavelet based denoising, (2) an acceleration based denoising, (3) a frequency based approach to estimate the heart rate followed by (4) a postprocessing step. Experiments with different movement types such as running and rehabilitation exercises were used for algorithm design and development. Evaluation of our heart rate estimation showed that a mean absolute error 1∶96 bpm (beats per minute) with standard deviation of 2∶86 bpm and a correlation of 0∶98 was achieved with our method. These findings suggest that the proposed methodology is robust to motion artifacts and is therefore applicable for heart rate monitoring during sports and rehabilitation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/EMBC.2015.7319787 | EMBC |
Field | DocType | Volume |
Computer science,Electronic engineering,Physical exercise,Acceleration,Heart rate | Conference | 2015 |
ISSN | Citations | PageRank |
1557-170X | 3 | 0.43 |
References | Authors | |
4 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Mullan | 1 | 3 | 0.43 |
| Christoph M. Kanzler | 2 | 3 | 0.43 |
| Benedikt Lorch | 3 | 3 | 1.10 |
| Lea Schroeder | 4 | 3 | 0.43 |
| Ludwig Winkler | 5 | 3 | 0.43 |
| Larissa Laich | 6 | 8 | 0.82 |
| Frederik Riedel | 7 | 3 | 0.43 |
| Robert Richer | 8 | 7 | 2.39 |
| Christoph Luckner | 9 | 3 | 0.43 |
| Heike Leutheuser | 10 | 22 | 5.19 |
| Björn M. Eskofier | 11 | 38 | 6.97 |
| Cristian F. Pasluosta | 12 | 61 | 5.99 |