## 3 Results and Discussion
### Workflow from Data Acquisition to Location Tracking
#### 3.1.2 Correlation and Regression of Trauma Scores and their Predictors  
In order to validate the hypothesis that there is a correlation between RTS, NEWS, and the Ps blunt or Ps penetrating scores, the statistical analysis had to be performed with an existing dataset. The MIMIC 2 Numerics dataset hosted by Physionet was used to perform the statistical analysis. The dataset was cleaned to extract features relevant to the NEWS and the RTS calculations according to patients admitted to the ICU and belonging to a clinical class category \"RESPIRATORY FAILURE\". Once the vital signs samples of all the patients belonging to a clinical category was extracted, columns for each of the vital signs were used for statistical analysis. The analysis was performed on 368,721 samples from eight patients admitted to ICU for the clinical class 'Respiratory Failure'. The samples were known to be taken at the interval of 1.024 s. It was observed that since the patients were admitted into ICU wards, the NEWS and the RTS scores agreed with their health status. The NEWS and the RTS severity scores less than 4 indicated the patients were under severe trauma, and this helped to accept the hypothesis. The MIMIC II database is widely accepted, and some experiments of similar nature have been performed on the dataset, so that it can be used for trauma score calculations. The vital signs and physiological parameters from Physionet and the MIMIC II Numerics (mimicdb/numerics) database were used to calculate NEWS and RTS, and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with \"respiratory failure\" and admitted to ICU. Figure 5: Spectral image of the respiratory rate (RR) waveform clearly showing two episodes of respiratory rate failure for Record 033n in the MIMIC Numerics database. NEWS and RTS scores showed no significant correlation (r = 0.25, \(p\) < 0.001) amongst themselves; however together, NEWS and RTS showed significant correlations with Ps (blunt) (r = 0.70, \(p\) < 0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, \(p\) < 0.001) and the NEWS score showed significant correlation (r = 0.79, \(p\) < 0.001) with Ps (blunt) scores, as shown in Table 3.  
Regression: Considering the age, heart rate, systolic BP, respiratory rate and SpO2 as predictors to PsBlunt, the predictors showed significant positive R for regression at F (5,368715) = 1098725, \(p\) < 0.001, total R\({}^{2}\) = 93%, as shown in Table 4.  
Both RTS and NEWS that were considered as variables to predict Ps had significant positive relationship with R for regression that was significant at F (2, 368,718) = 442,679.9, \(p\) < 0.001, total R\({}^{2}\) = 70%. There was no significant correlation between NEWS and RTS (r = 0.25, \(p\) < 0.001), which was due to the limitations of the sample space belonging to a particular clinical class. An extensive regression analysis over the entire 'Numerics' dataset would be necessary to establish an affirmative correlation between NEWS and RTS scores. There may not be a higher degree of correlation between NEWS and RTS scores themselves, which was due to the sample space considered from a single 'clinical class' for analysis. If the most recent MIMIC II/III Numerics dataset, which has more than 68,000 records where each record covers more than 72 hr, an affirmative and significant relationship between RTS and NEWS could potentially be observed. There was, however, a significant positive relationship between NEWS and PsBlunt (r = 0.79, \(p\) = 0.01) and a moderate positive relationship between the RTS and PsBlunt (r = 0.63, \(p\) = 0.01). Correlation and regression between Age, HeartRate, SpO2, SysBP and PsBlunt: There were positive significant relationships between Age (r = 0.91, \(p\) = 0.01), HeartRate (r = 0.87, \(p\) = 0.01), and SpO2 (r = 0.94, \(p\) = 0.01) with PsBlunt. There were weak positive relationships between SysBP (r = 0.47, \(p\) = 0.01) and Respiratory Rate (r = 0.33, \(p\) = 0.01) with PsBlunt.