## 4 Conclusions  
The research began with the questions and the hypothesis of being able to calculate trauma scores for patients under trauma, using wearable sensor kits, non-invasively in real time and importantly, under no ambulatory or hospitalization settings. It was also hypothesized that given the vital signs plus physiological information about the patient and the corresponding trauma scores, the prediction of survival scores could be estimated, which, could help the critical care team at a remote location to prepare for the emergency procedures. To enable such a system to function without key infrastructural changes, location information had to be garnered, and the information payload had to be transmitted in real time and using standard telemetry protocols and clinical coding standards. The four main vital signs like temperature, pulse rate/heart rate, respiration rate (rate of breathing), blood pressure (non-invasive systolic) and oxygen saturation were adequate to calculate the trauma scores and prediction of survival [15; 16]. From the results obtained, stationary wavelet transform, along with the passband Chebyshev Type II order 2 filter, turned out to be effective in removing signal noise, motion artefacts, and baseline wandering in ECG waveforms, and yielding a better signal-to-noise ratio. The respiration rate and blood pressure as vital signs were important physiological parameters, and in the absence of a sensor that can directly and non-invasively measures these parameters, it was found that the ECG-derived measures could be used. Respiratory rate was calculated using the Physionet WFDB library using the EDR utility, and the derived value has a high correlation to the measured values, as found by the authors of the library. [40] The EDR utility is a C program and it can be compiled on any GNU/Unix/Linux platforms. BP is also an important vital sign that is used in trauma scoring, and in the absence of an integrated sensor; there were challenges in measuring the BP and in removing motion artefacts [41]. The PTT, which requires the synchronization of ECG and SpO2 sensor measurements, could have been used, though the synchronization in the two sensor readings may lead to inaccuracies. Besides, the BP samples are not required as frequently as the SpO2 or ECG samples, so an external BP monitor can be used. Pulse oximetry, as the fifth vital sign [20] was also used in calculating trauma scores, and it was required in trauma calculations and in the prediction of survival assessment. The pulse oximetry sensor can capture SpO2 readings non-invasively. The correlation and regression scores between NEWS, RTS, and Ps scores were studied, and a high degree of regression was observed between the NEWS and RTS, taken together with the Ps. There may  
Figure 6: The shortest route algorithm using the QGIS Network Analysis tool indicating the shortest route between the start- and the end-points. In the figure, (**a**) shows the start and the end point latitude and longitude and the distance and time it would take to reach the end point; (**b**) shows the rendered map layer with the track between the two end points. not be a higher degree of correlation between the NEWS and RTS themselves, though this may have been due to the sample space, as 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. This however, would require resources with higher computing power and storage. In the calculations for injury severity scores, TRISS [22] remains the most commonly used tool for benchmarking trauma fatality outcome. The predictive power of TRISS could be substantially improved by re-classifying the measured parameters, and by altering the coefficients for the sample space belonging to a particular clinical class.