## 2 Materials and Methods
### Composite Sensor Kit for Real-Time Multiparameter Physiological Data Acquisition
Figure 2: The signal waveforms (**a**) for the vital signs of heart rate, respiratory rate, SpO2 (pulse oximeter) and systolic blood pressure for Record 033n of the MIMIC Numerics database. The power spectrum also shows a cutoff at a frequency of 0.25 (normalized in radians/sample). The composite sensor kit (**b**) showing the ECG sensor, SpO2, and the GPS/GNSS (positioning) sensor boards, to capture vital signs from human subjects. Breathing rate (BR) or the respiratory rate (RR) are key physiological parameters that are used in calculating trauma scores, and they are still widely measured by counting the breaths manually. Quite a few algorithms have been proposed to estimate the RR from the electrocardiogram (ECG) and pulse oximetry (Photoplethysmogram, PPG) signals. These RR non-invasive methods of RR estimation are applicable in both healthcare and fitness monitoring [39]. BP is also an important vital sign used in trauma scoring, and in the absence of an integrated sensor, there are challenges due to the measured BP, and the removal of motion artefacts. Researchers have tried to overcome this challenge by analyzing oscillometric pulses after amplitude modulation and amplification of the ECG signal [41]. The PTT feature, which is calculated from ECG and PPG signal, could be used for BP estimation. A prototype was developed and tested, which achieved a mean absolute difference of less than '5' mm Hg for estimating the BP with the reference Omron BP monitor. It could be hypothesized that in the absence of RR and BP sensors that can be readily integrated with the composite wearable sensor kit, the estimated values of RR and BP that are derived from ECG waveforms could be used to calculate the trauma and injury scores. PTT can be measured as the time interval between the R-peak in an ECG wave and a characteristic point at predetermined thresholds of the PPG signal in the same cardiac cycle [42]. By using PTT, the systolic blood pressure (SBP) can be calculated as follows: \[\text{SBP}=4.8008600358\times 10^{4}\times\text{PTT}+1.308532932\] The steps for data acquisition and WFDB-compatible record generation are as follows:  
1. Read the samples from the ECG and PPG sensor using the Adafruit GPIO library for BBB with a sampling frequency of 1024 Hz. At least a 3-millisecond interval should be present between the two samples. 2. Use the 'wrsamp -F 360 -G 1000 -i' command from WFDB to convert these samples to a WFDB-compatible record where '1000' is the gain. The WFDB record conversion sampling frequency is 360 Hz, which is different from the data acquisition sampling frequency of 1024 Hz. ECG strips are analyzed in batches of 10 s intervals. 3. Use the 'gqrs -r ecg_samples_file -m 1' command to obtain the QRS complexes from the samples. The threshold set is set to '1 mV', and a qrs annotation file is generated. 4. Use the 'rdann -a qrs -r ecg_samples_file' command, which uses the 'qrs' annotation file to find the R-peaks. 5. Find the number of samples between the R-peak and the next available PPG peak, and calculate the time corresponding to the sampling interval, which would give the PTT value that is used to determine the systolic BP. At 360 Hz, the WFDB sampling frequency interval between two consecutive samples would be approximately 2.75 milliseconds. 6. Use 'edr -r edr_samples_file -i qrs_file -f 0 -t 0.16:0' to generate the EDR samples for a value of '0.16', corresponding to 10 s time frames and corresponding to the 'qrs' annotation file. This would give the average respiratory rate over a 10 s interval. Thus, using the ECG and PPG sensor modules, all the vital signs could be measured from a human subject. However, for the purpose of calculation of trauma scores and the prediction of survival, and to study the relationship amongst these scores, readings from a patient who had undergone trauma were required. The Physionet MIMIC II Numerics (vital signs) dataset was used for this purpose. The statistical analysis and trauma scoring was performed on the dataset from MIMIC II in the absence of a real trauma patient.