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Error code: DatasetGenerationError Exception: ArrowInvalid Message: Float value 0.123 was truncated converting to int64 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp> arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper return func(array, *args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature return array_cast( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper return func(array, *args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, in array_cast return array.cast(pa_type) File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast return call_function("cast", [arr], options, memory_pool) File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Float value 0.123 was truncated converting to int64 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1524, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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@odata.context
string | frequency
int64 | dataX
sequence | data
list | segmentsData
dict |
---|---|---|---|---|
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
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http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.19204919820271385,-0.16168700097028402,-0.13357405875984235,-0.1087178621(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.06444768114603353,-0.06460485273116512,-0.05416165717299959,-0.0322357479(...TRUNCATED) | {"pqAverageValue":0.123,"qtAverageValue":0.3539,"stAverageValue":0.1244,"pqIntervals":[{"indexOfFirs(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.10450544410468408,0.10260305300709255,0.09595574124681122,0.08463284439636(...TRUNCATED) | {"pqAverageValue":0.0967,"qtAverageValue":0.365,"stAverageValue":0.1195,"pqIntervals":[{"indexOfFirs(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.014993661467586974,0.008662880801621389,0.004641376230735654,0.00414176094(...TRUNCATED) | {"pqAverageValue":0.0874,"qtAverageValue":0.3602,"stAverageValue":0.0858,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.048094163254367536,-0.04386362830406088,-0.040033151275657666,-0.03640048(...TRUNCATED) | {"pqAverageValue":0.0825,"qtAverageValue":0.3671,"stAverageValue":0.0785,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.1720298608457035,-0.1381471928533835,-0.06007278530980501,0.0688884425330(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.1131532094846479,0.11222595037012538,0.11013556269575805,0.106072310579041(...TRUNCATED) | {"pqAverageValue":0,"qtAverageValue":0,"stAverageValue":0,"pqIntervals":[],"qtIntervals":[],"stSegme(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[-0.1901804469911864,-0.14616334658338984,-0.0879028916014203,-0.020309804042(...TRUNCATED) | {"pqAverageValue":0.0658,"qtAverageValue":0.3796,"stAverageValue":0.1139,"pqIntervals":[{"indexOfFir(...TRUNCATED) |
http://192.168.6.15/api/v4/$metadata#Medmon.EcgDataApi
| 500 | [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.(...TRUNCATED) | [{"title":"I","values":[0.21822900584593816,0.2105110505712715,0.20564538459138082,0.200052671310298(...TRUNCATED) | {"pqAverageValue":0.0563,"qtAverageValue":0.3772,"stAverageValue":0.11,"pqIntervals":[{"indexOfFirst(...TRUNCATED) |
MCD-rPPG: Multi-Camera Dataset for Remote Photoplethysmography
This repository contains the dataset from the paper "Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation"
The presented large-scale multimodal MCD-rPPG dataset is designed for remote photoplethysmography (rPPG) and health biomarker estimation from video. The dataset includes synchronized video recordings from three cameras at different angles, PPG and ECG signals, and extended health metrics (arterial blood pressure, oxygen saturation, stress level, etc.) for 600 subjects in both resting and post-exercise states.
We also provide an efficient multi-task neural network model that estimates the pulse wave signal and other biomarkers from facial video in real-time, even on a CPU.
The MCD-rPPG Dataset
The dataset contains:
- 3600 video recordings (600 subjects × 2 states × 3 cameras)
- Synchronized PPG (100 Hz) and ECG signals
- 13 health biomarkers: systolic/diastolic pressure, oxygen saturation, temperature, glucose, glycated hemoglobin, cholesterol, respiratory rate, arterial stiffness, stress level (PSM-25), age, sex, BMI.
- Multi-view videos: frontal webcam, FullHD camcorder, mobile phone camera.
Code and usage
See GitHub repository https://github.com/ksyegorov/mcd_rppg
Citation
If you use the MCD-rPPG dataset or code from this repository, please cite our work:
@article{egorov2024gaze,
title={Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation},
author={Egorov, Konstantin and Botman, Stepan and Blinov, Pavel and Zubkova, Galina and Ivaschenko, Anton and Kolsanov, Alexander and Savchenko, Andrey},
journal={arXiv preprint arXiv:2508.17924},
year={2024}
}
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