An Efficient Emergency Patient Monitoring based on Mobile Ad Hoc Networks

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.

Flexible complementary circuits operating at sub-0.5 V via hybrid organic–inorganic electrolyte-gated transistors

Electrolyte-gated transistors (EGTs) hold great promise for next-generation printed logic circuitry, biocompatible integrated sensors, and neuromorphic devices. However, EGT-based complementary circuits with high voltage gain and ultralow driving voltage (<0.5 V) are currently unrealized, because achieving balanced electrical output for both the p- and n-type EGT components has not been possible with current materials. Here we report high-performance EGT complementary circuits containing p-type organic electrochemical transistors (OECTs) fabricated with an ion-permeable organic semiconducting polymer (DPP-g2T) and an n-type electrical double-layer transistor (EDLT) fabricated with an ion-impermeable inorganic indium–gallium–zinc oxide (IGZO) semiconductor. Adjusting the IGZO composition enables tunable EDLT output which, for In:Ga:Zn = 10:1:1 at%, balances that of the DPP-g2T OECT. The resulting hybrid electrolyte-gated inverter (HCIN) achieves ultrahigh voltage gains (>110) under a supply voltage of only 0.7 V. Furthermore, NAND and NOR logic circuits on both rigid and flexible substrates are realized, enabling not only excellent logic response with driving voltages as low as 0.2 V but also impressive mechanical flexibility down to 1-mm bending radii. Finally, the HCIN was applied in electrooculographic (EOG) signal monitoring for recording eye movement, which is critical for the development of wearable medical sensors and also interfaces for human–computer interaction; the high voltage amplification of the present HCIN enables EOG signal amplification and monitoring in which a small ∼1.5 mV signal is amplified to ∼30 mV.

A Machine Learning SDN-Enabled Big Data Model for IoMT Systems

In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.

Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

Physiological signals retrieve the information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time and thus proper treatment can be made possible. With the addition of Internet of Things in healthcare, real-time data collection and pre-processing for signal analysis has reduced burden of in-person appointments and decision making on healthcare. Recently, Deep learning-based algorithms have been implemented by researchers for recognition, realization and prediction of diseases by extracting and analyzing the important features. In this research real-time 1-D timeseries data of on-body non-invasive bio-medical sensors have been acquired and pre-processed and analyzed for anomaly detection. Feature engineered parameters of large and diverse dataset have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring the implemented system uses wavelet time scattering features for classification and deep learning based autoencoder for anomaly detection of time series signals for assisting the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through bio-medical sensors using signal analysis techniques for anomaly detection and timeseries classification has been done for the disease prognosis in real-time. Wavelet time scattering based signals classification accuracy of 99.88% is achieved. In real time signals anomaly detection, 98% accuracy is achieved. The average Mean Absolute Error loss of 0.0072 for normal signals and 0.078 is achieved for anomaly signals.

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.