Comments (9)
Increasing the sampling rate to 29 and bumping the block size from 40 to 200, I get the following metrics after estimating heart rate from the ground truth ordinal values:
Pearson r is still a bit off in comparison with the results from your paper:
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Seems like there is a bug or a typo, since filtered_sig
is assigned but never used:
Line 137 in bab00d9
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Here are the metrics I get after running get_bpm
on a sample of discretized PPG and heart rates from UBFC using the batch size of 8 and slicing a video segment of 6s into 60 frames:
The metrics seem way off. Could you please share how you have converted predicted ordinal values to heart rate?
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Here are the ideal metrics (obtained after running get_bpm
on the ground truth segments for blocks of 60 frames) after switching to a higher sampling rate for the frames fed to the model (sampling on the (60/25)s timeframe). The result is still disappointing:
Could you please comment on the ideal results you get with your post-processing?
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Hi there,
Thanks for taking interest on our work. May I ask what's the training data that you used to train the model? We have only provided a training dataset containing 2 users, which is not the entire dataset.
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The metrics above are for videos from the UBFC dataset that have frame rates greater than 25 fps. Batches of 60 frames were taken in each time period of 2.4 seconds, with the sliding window step of 1 second, and the corresponding ground truth ppg signals segmented into 40 regions which were then used to estimate heart rate using get_bpm from your repo. This estimated heart rate was then compared with the ground truth pulse from UBFC, which gave the results above (this approach of measuring mae/r2 was recently used for evaluation in the cvpr 2020 workshop on remote sensing). What kind of metrics do you get when you segment the ground truth ppg into ordinal values and use them for predicting pulse? Or did you avoid using ground truth pulse from ubfc in your comparison?
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Hi there,
the UBFC dataset we received from the author didn't contain the ground truth ppg. So we only take use of the ground truth ordinal values. The get_bpm function is for the model. The inaccuracy may be caused by a few reasons, first one is the low sampling rate (24 fps), second is that the sliding window for prediction is too small. You can use a bigger sliding window and a higher sampling rate when predicting in frequency domain.
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Thanks! So you used just the ordinal values for estimating heart rate on the UBFC dataset and comparing it with predicted values? In no way the ground truth for pulse was used for evaluation?
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How do you train this network and what data sets are used? I use the deap dataset, which is difficult to converge.
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Related Issues (20)
- Excuse me, Could you upload "test" code?
- Real time on webcam? HOT 2
- Inference pipeline
- Error HOT 8
- What should be the input for the method set_input_for_test?
- Why does the test set used in the example contain a video fragment of a subject that appears in the training?
- "Algorithm 1 Training of Meta-Learner"
- UBFC Video - In which part of the code we need to pass it ? HOT 1
- train on VIPL dataset
- Pretrained HOT 1
- Pretrained model
- Help! testing model HOT 3
- 作者可以把example.pth的连接重新放一下吗,已经失效了 HOT 2
- Example.pth link not working HOT 5
- PreProcessing Issue
- How to produce example.pth?
- RuntimeError: result type Float can't be cast to the desired output type Byte HOT 1
- Custom Dataset Template file not found
- This model is very difficult to train. What training skills are there? HOT 5
- about AttentionNet HOT 1
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