###### Abstract
The MATLAB Digital Signal Processing toolbox was used to perform signal conditioning, and the MATLAB Coder tool was used to convert the MATLAB code to the corresponding C/C++ code, which could be compiled on the TI AM335x (Beaglebone Black, Texas Instruments-BeagleBoard, Arrow Electronics Inc., Centennial, CO, USA). The MATLAB environment and SPSS statistical tools are largely been used for waveform and statistical analyses. Since the parameters being measured were medical in nature, a software library that is widely accepted by the biomedical research community was used. Physionet provides all of the software tools and dataset from real patients for data analysis research that was related to physiological parameters [8; 9]. The composite sensing device could measure vital signs in real time; however, in order to perform data analysis, to calculate trauma scores based on vital signs and physiological parameters, the device would have to be tested on real trauma patient. In the absence of patient availability, a database of vital signs and physiological parameters of patients admitted to ICU under trauma conditions had to be considered. The Physionet MIMIC II Numerics database was used to develop the correlation and regression models to a establish relationship between the trauma scores and the prediction of survival. The NEWS, RTS and TRISS scores were found to be quite useful in estimating the probability of survival in the patients. There is enough evidence in more recent studies that these trauma scores have a direct correlation with the prediction of survival of the patient [10; 11; 12; 13]. Following the trauma scores calculations, adding location awareness features to the sensor kit was illustrated by using Quantum GIS and Global Positioning System (GPS) tools and techniques, in order to calculate the shortest route from the subject's current position, to the nearest healthcare provider using GIS tools and techniques. For a composite healthcare monitoring kit to be useful to patients, as well as the healthcare service providers, the readings and trauma scoring have to be transmitted to the healthcare service providers using a standard telemetry protocol. Furthermore, the readings and the trauma experience has to be encoded in a standard coding system that is widely acceptable within the medical community. Health Level 7 (HL7)-based Fast Health Interoperability Resources (FHIR) interoperable software components have been used to encode the events related to trauma as observations that can be logged to the electronic health record databases in real time. The research demonstrates the use of the FHIR servers [14], and the development and generation of the trauma event context and observation models for EHR interoperability. Due to the importance of trauma scoring in emergent situations and for the prediction of survival calculations, it became necessary to derive relationship between the trauma scores and physiological parameters [10; 11]. In the discussion and results section, the correlation and regression relationship between the vital signs and trauma scores has been discussed. There has been a focus on remote health monitoring systems based on the Internet of things (IoT) technology, and this concept is being accepted and adopted by private and public healthcare service providers. This has resulted in the reduction of healthcare costs, and at the same time an improvement in healthcare for aging patients and patients with chronic diseases. It also assists in reducing accommodation requirements in hospitals and private healthcare providers. This continuously monitored information, however, would have limitations in terms of diagnosis if there were no emergency-related trauma scores available to the critical care team ahead of time. The research endeavors to bridge this gap between the monitoring, diagnosis, and timely treatment in trauma-related events.