###### Abstract
Moreover, the vital signs and triage information is collected, and preparations are made after the patient has been carried to the hospital, which in cases like cardiac arrest, could be too late. Other vital signs such as the blood pressure and respiratory rate are not measured by these kits, and they are measured after the patient is admitted to the ICU after the trauma episode has occurred, which could cause a delay in treating the patient. The composite devices in this research not only monitor individual physiological parameters, but they also calculates related physiological parameters and vital signs. The device uploads the trauma event information and triage information to the electronic health records (EHR) in real time, and it can raise alarms well in time. Moreover, location awareness is built into the device, which locates the nearest healthcare service provider and calculates the shortest path to reaching the healthcare provider. Vital signs are useful in detecting or monitoring medical problems. Vital signs can be measured in a medical setting, at home, at the site of a medical emergency, or elsewhere [4]. With more and more wearable kits that have become easily available, manual intervention in healthcare monitoring has been reduced in many hospital and prehospital settings. With an increasing number of miniaturized Internet of things (IoT) devices, Analogue Front-End (AFE) modules and digital signal processing (DSP) devices that have surfaced recently, vital signs and other physiological parameters can be easily measured non-invasively; and the readings can be analyzed in a networked environment to provide a managed health monitoring system [4]. Increasing the use of machine learning algorithms and neural networks on vital sign data can determine the deterioration of health in a patient, and can help to predict the health status ahead of time, in order to prepare for an emergency [5]. Several hospitals and healthcare service providers use standard trauma scoring mechanisms to ascertain or at least estimate patient health, though these are restricted to hospital or ambulatory settings. Scoring measures such as the National Early Warning Score (NEWS), the Glasgow Coma Scale (GCS), the Injury Severity Score (ISS), the Trauma and Injury Severity Score (TRISS) and the Simplified Acute Physiologic Score (SAPS) II/III have been successfully used to identify high health risks and the extent of trauma in patient ICU settings [6; 7]. These scores in turn can estimate the probability of survival (Ps) in a patient, and can help the critical care team to prepare for emergency procedures ahead of time, though due to limitations in the size of equipment, this has remained restricted to hospitalized settings. The research focuses on the development of a composite healthcare monitoring device that a patient can wear in case of emergency. The device would capture vital signs and other physiological information, and transmit this telemetry data to the electronic health records, and can raise alarms to prepare for medical emergency. In the problems and challenges section, the problems related to measuring and filtering vital signs have been discussed, along with the solutions to calculate other vital signs such as blood pressure and respiratory rate, which are commonly measured only in hospitalized settings. The readings taken from the human subjects are in a time series format, and they have noise and motion artefacts. In order to perform an accurate waveform and statistical analysis, the data should be clean and accurate. In the Materials and Methods section, the research subsequently illustrates the use of data acquisition,filtering, smoothing, and quantization-error removal techniques to extract the portion of the signal that is error free.