Wrist PPG During Exercise
Data Type：2D Box,Pose
Data Preview ? 49.8M
Data Structure ?
This database contains wrist PPGs recorded during walking, running and bike riding. Simultaneous motion estimates are collected using both accelerometers and gyroscopes to give multiple options for the removal of motion interference from the PPG traces. A reference chest ECG is included to allow a gold-standard comparison of heart rate during exercise.
Measurements were taken using an ECG unit placed on the chest together with a PPG and Inertial Measurement Unit placed on the left wrist while participants used an indoor treadmill and exercise bike.
Single channel, two electrode, ECG recordings were taken using an Actiwave (CamNtech, Cambridge, UK) recorder and pre-gelled self-adhesive Silver-Silver Chloride (Ag/AgCl) electrodes as are standard for ECG monitoring. These were placed on the upper chest with one electrode on either side of the heart. R peaks in this ECG trace were identified by hand and these times are included in the database to allow a gold standard reference heart rate comparison. These R peak times are referenced assuming the first sample in the ECG trace occurs at time 0 s.
PPG and motion data were recorded using a Shimmer 3 GSR+ unit (Shimmer Sensing, Dublin, Ireland). This contains a gyroscope, a low noise accelerometer, a wide range accelerometer, and a magnetometer integrated into a single package. The PPG sensor was glued to the main Shimmer unit in order to give a rigid connection and allow the movement sensors inside the main Shimmer unit to accurately record the movement of the PPG sensor. The combined unit was then placed on the left wrist in approximately the position of a standard watch.
Participants were asked to perform one or more different types of exercise. Four options were available:
- walking on a treadmill at a normal pace for up to 10 minutes.
- light jog/run on a treadmill, at a pace set by the participant, for up to 10 minutes.
- pedal on an exercise bike set a low resistance for up to 10 minutes.
- pedal on an exercise bike set at a higher resistance for up to 10 minutes.
The objective was to introduce a range of representative motion artifacts into the collected heart signals, not to carry out a set exercise routine. As such each participant was free to set the pace of the treadmill and pedal rate on the bike so they were comfortable and also to change these settings or stop the exercise at any time. Most participants spent between 4 and 6 minutes on each activity. In all cases the subject was starting from rest. All signals were sampled at 256 Hz. Records from 8 participants are present (3 male, 5 female), aged 22--32 (mean 26.5).
For the walking and running records, the database contains the raw PPG and motion signals present after segmentation into the appropriate activity. No filtering is applied, beyond that built in to the Shimmer hardware. For the cycling records, large amounts of high frequency noise were present in the PPG traces. Prior to conversion to WFDB format the cycling PPG traces were low pass filtered using a second order IIR Butterworth digital filter with 15~Hz cut-off and zero group delay with the Matlab filtfilt command. All ECG records have a 50 Hz notch filter applied as part of the Actiwave control software to remove mains interference.
Further details and illustrative example signals can be found in the reference paper
PPG, motion, and ecg recordings are provided in WFDB format, with record names reflecting the subject number and the activity. WFDB annotation files of the reference ECG r-peaks are also provided.
This data was contributed to Physionet by Alexander J Casson from the University of Manchester, School of Electrical and Electronic Engineering.
When using this resource, please cite the original publication:
Delaram Jarchi and Alexander J. Casson. Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion. Data 2017, 2(1), 1; doi:10.3390/data2010001
Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.