Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient’s emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician’s burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.
Internet of Things (IoT) based healthcare system is now at the top peak because of its potentialities among all other IoT applications. Supporting sensors integrated with IoT healthcare can effectively analyze and gather the patients’ physical health data that has made the IoT based healthcare ubiquitously acceptable. A set of challenges including the continuous presence of the healthcare professionals and staff as well as the proper amenities in remote areas during emergency situations need to be addressed for developing a flexible IoT based healthcare system. Besides that, the human entered data are not as reliable as automated generated data. The development of the IoT based health monitoring system allows a personalized treatment in certain circumstances that helps to reduce the healthcare cost and wastage with a continuous improving outcome. We present an IoT based health monitoring system using the MySignals development shield with (Low power long range) LoRa wireless network system. Electrocardiogram (ECG) sensor, body temperature sensor, pulse rate, and oxygen saturation sensor have been used with MySignals and LoRa. Evaluating the performances and effectiveness of the sensors and wireless platform devices are also analyzed in this paper by applying physiological data analysis methodology and statistical analysis. MySignals enables the stated sensors to gather physical data. The aim is to transmit the gathered data from MySignals to a personal computer by implementing a wireless system with LoRa. The results show that MySignals is successfully interfaced with the ECG, temperature, oxygen saturation, and pulse rate sensors. The communication with the hyper-terminal program using LoRa has been implemented and an IoT based healthcare system is being developed in MySignals platform with the expected results getting from the sensors.
A gap coupled hexagonal split ring resonator (GCHSRR) based metamaterial is presented in this paper for S-band and X-band microwave applications with absorptance. This gap coupled hexagonal split ring resonator is the amendment of the typical split-ring resonator (SRR). Three interconnected hexagonal split ring resonators are applied with a stripline to increase the electrical length and coupling effect of the GCHSRR. SRR has an impact on the extraction of effective parameters such as permittivity, permeability and refractive index. The dimension of the proposed GCHSRR unit cell is 10 × 10 mm 2, which is printed on low-cost FR4 material. The transmission frequency of the proposed GCHSRR unit cell ranges from 3.42 GHz to 3.73 GHz and 11.27 GHz to 11.91 GHz, which makes the metamaterial applicable for S-band and X-band microwave applications. The GCHSRR unit cell has a double negative regime of 7.92 GHz to 9.78 GHz with an effective negative refractive index regime of 6.30 GHz to 10.22 GHz and 11.97 GHz to 12.61 GHz. The effective medium ratio is 8.4, which implies the novelty of the proposed design. Moreover, the GCHSRR has high absorption peaks of 99%, 98%, and 81% at 4.27 GHz, 5.42 GHz, and 12.40 GHz, respectively. An 18 × 20 GCHSRR array structure is also designed and studied. The effective parameters and the effective medium ratio with a high absorptance make the proposed GCHSRR based metamaterial suitable for practical microwave applications.INDEX TERMS Split ring resonator, metamaterial, absorptance, effective medium ratio.
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