The first frame of every video recording must not be used. The dataset covers the following challenges: (i) large subject's motion (possibly periodic) in all three axis, (ii) rapid motions inducing motion blur, (iii) strong facial expressions, (iv) wearing glasses, (v) non-uniform lighting, (vi) light interference, (vii) atypical non-frontal camera angles. The lowest 56 bpm, the highest 159 bpm, the mean 108.96 bpm, standard deviation 23.33 bpm Three lighting setups were used: (i) natural light coming from a nearby window, (ii) 400W halogen light and (iii) 30W led light Speaking, rowing, exercising on a stationary bike and on an elliptical trainer speaking and rowing performed twice - once with a halogen lighting resulting in a strong 50Hz (100Hz) temporal interference, once without
#ECG SIGNAL DOWNLOAD PRO#
Two-lead Viatom CheckMe™ Pro device with CC 5 lead One Logitech camera was attached to the currently used fitness machine, the other was positioned on a tripod as close as possible to the first camera Two RGB Logitech C920 webcameras - 30fps, 1920 x 1080 pixels stored in an uncompressed YUV planar pixel format (approx. Realistic corpus of subjects performing physical activities on fitness machinesĦ x 1 minute video recording (no sound) + ECG per subject (207 videos in total)ġ7 subjects (14 male, 3 female) with age range 20 to 53 years It is distributed in a single file compressed with a 7zip program.
#ECG SIGNAL DOWNLOAD DOWNLOAD#
The evaluation and learning scripts are published at my public repository.ĭownload the request form, print it, sign it and send it to Once received, we will share you a download link. The models yielding the results presented in the BMVC paper can be downloaded here. In Proceedings of British Machine Vision Conference, 2018. (2018) Visual Heart Rate Estimation with Convolutional Neural Network. The raw ECG signals are rather noisy and contain both high and low frequency noise components.If you consider using the dataset, please reference the following: Spetlik, R., Franc, V., Cech, J. The number of records for each person varies from 2 (collected during one day) to 20 (collected periodically over 6 months). The records were obtained from volunteers (44 men and 46 women aged from 13 to 75 years who were students, colleagues, and friends of the author). hea file for the record) containing age, gender and recording date. 10 annotated beats (unaudited R- and T-wave peaks annotations from an automated detector).ECG lead I, recorded for 20 seconds, digitized at 500 Hz with 12-bit resolution over a nominal ☑0 mV range.The database contains 310 ECG recordings, obtained from 90 persons. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.
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