Internet of Things (IoT) for Healthcare Application: Wearable Sleep Body Position Monitoring System Using IoT Platform

People with health conditions such as dementia often face problems sleeping, experience wake-rest routine changes and suffer from emotional disturbances amid sleep disorders. Caregivers, such as family members, of dementia sufferers face great challenge in taking care of such patients at night as it takes a toll on their own sleep quality resulting in sleep deprivation and other issues. The goal of this work, presented in this paper, is to develop a wearable body position monitor that can detect user's body position and keep online records during sleep; provide light when data shows the user is not asleep, aid user to fall asleep with audio assist feature, and if necessary, activate emergency alert call to caregivers when the patient remains seated or stands for longer durations (e.g. more than 20 minutes) at night. Main system components of the developed prototype include MySignals HW Complete Kit (e-health medical development platform), Arduino Uno microcontroller, LEDs, speakers, micro SD card, micro SD card reader, SPI interface and esp8266 module. Real-time transmission, data analysis and visualization and remote data storage has been realized. The plan for the next phase of this work will include application of sleep pattern recognition and machine learning techniques on large datasets and real biometric measurements

Internet of Things (IoT) Enabled Smart Indoor Air Quality Monitoring System

This article introduces development of a system that monitors indoor air quality by using Internet of Things (IoT) technology. The objective of this system is to monitor and improve indoor air quality automatically, i.e. with minimum human intervention. The system contains physical circuit and an interactive platform. Main components used in physical circuit are Arduino Leonardo, Dust Sensor, Temperature and Humidity Sensor, LCD Display and Fan. Interactive platforms involved are The Things Network and Ubidots. Principal parameters of interest are sensed by physical circuit and converted into Air Quality Index (AQI), which is then sent to an interactive platform via gateway. After estimating AQI, the Interactive platform triggers events based on certain predetermined conditions to improve air quality through SMS alerts and circuit actuators

Artificial Intelligence enabled Smart Refrigeration Management System using Internet of Things Framework

Design of an intelligent refrigeration management system using artificial intelligence and Internet of Things (IoT) technology is presented in this paper. This system collects the real-time temperature inside the refrigeration implement, record the information of products and enhance function of refrigerators through the application of Internet of Things technology to facilitate people in managing their refrigerated and frozen groceries smartly. The proposed system is divided into two parts, On-board sub-system and Internet based sub-system. An Arduino Leonardo board is used in onboard sub-system to control other components including low power machine vision OpenMV module, temperature & Humidity sensor, and GY-302 light intensity sensor. OpenMV camera module is used for recognizing types of food, reading barcodes and OCR (optical character recognition) through convolution neural network (CNN) algorithm and tesseract-ocr. The food type identification model is trained by the deep learning framework Caffe. GY-302 light intensity sensor works as a switch of camera module. DHT11 sensor is used to monitor the environmental information inside the freezer. The internet based sub-system works on the things network. It saves the information and uploads it from onboard sub-system and works as an interface to food suppliers. The system demonstrates that the combination of existing everyday utility systems and latest Artificial Intelligence (AI) and Internet of Things (IoT) technologies could help develop smarter applications and devices

Intelligent Instruction-Based IoT Framework for Smart Home Applications using Speech Recognition

Design of a smart home using Internet of Things (IoT) and Machine Learning technology has been presented in this paper. This design is primarily based on LoRaWAN protocol and the main objective of this work was to establish an IoT network that is based on integration of sensors, gateway, network server and data visualization system. More importantly, intelligent speech recognition system is designed and presented here in detail as part of this work to achieve a novel futuristic smart home system design framework with intelligent instruction-based operation mechanism. In the case of low noise, the success rate of speaker recognition is above 90% based on THCHS-30 dataset

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design

IoT Enabled Smart Security Framework for 3D Printed Smart Home

Recently, smart home design using Internet of Things (IoT) technology has become a growing industry. Since security is the most important element of the smart home design, the project aims to design a 3D printed smart home with a focus on the security features that would meet the security design of futuristic real homes. The surveillance system of traditional smart home is separated from the door lock system. This project innovatively integrates and coordinates them through the facial recognition algorithms, which forms the entry system of this design. The overall system can be divided into two subsystems (parts), which are the sensing and actuation system (PART I) and the entry system (PART II). PART I includes various sensors and actuators to ensure the security of home, including combustible gas sensor, air quality sensor and temperature & humidity sensor. When anomalies are detected by sensors, actuators such as ventilator, buzzer and LEDs start to work. In PART II, the PIR motion sensor is utilized to detect the person to activate the facial recognition step. Facial recognition algorithm (LBPH algorithm) is implemented for person classification, which is used in selecting the duration of recording for the surveillance system. The surveillance system could select not to record for the occupants or different levels of recording for each occupant based on the confidence of recognition. The project outcomes a 3D printed smart home with a door lock system, a surveillance system, and a sensing & actuation network, which accomplishes the security features in perception and network layer of IoT system design

Strategic priorities for hematopoietic stem cell transplantation in the EMRO region

The World Health Organization-designated Eastern Mediterranean region (EMRO) consists of 22 countries in North Africa and Western Asia with a collective population of over 679 million. The area comprises some of the wealthiest countries per capita income and some of the poorest. The population structure is also unique and contrasts with western countries, with a much younger population. The region sits in the heart of the thalassemia belt. Many countries have a significant prevalence of sickle cell disease, and cancer is on the rise in the region. Therefore, the strategic priorities for the growth and development of hematopoietic stem cell transplantation (HSCT) differ from country to country based on resources, healthcare challenges, and prevalent infrastructure. Thirty-one reporting teams to the Eastern Mediterranean Blood and Marrow Transplantation Group have active HSCT programs in 12 countries; allogeneic transplants outnumber autologous transplants, and the proportion of allotransplants for non-malignant conditions is higher in the EMRO region than in Western Europe and North America. The vast majority (99%) of allotransplants are from matched related donors. Matched unrelated donors and other alternate donor transplants are underutilized. The chance of finding a matched related donor for allografts is higher, with a significant chance of finding matched donors among non-sibling related donors. Reasons for relatively lower rates of transplants compared with other countries are multifactorial. Capacity building, development of newer centers, innovative funding, and better utilization of information technology are required to make transplantation as an accessible modality to more patients. Cost-effectiveness and cost-containment, regulation, and ensuring quality will all be priorities in planning HSCT development in the region

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping

Assessment and Feedback Under Disruptive Circumstances in Trans-National Education

The COVID-19 pandemic outbreak and the lock-down and social distancing strategies adopted to contain it have drastically affected our daily lives and the routine businesses. Provision of educational services in a continuous and useful manner in such circumstances is a massive challenge and requires innovative methods. Effective assessment and feedback play a pivotal role in traditional teaching and learning approaches and it is of even more vital importance in disruptive conditions. This paper discusses different assessment and feedback techniques in the online delivery of higher education courses in lockdown scenarios. The effectiveness of these approaches is evaluated through qualitative and quantitative study of student and staff feedback for an engineering course being delivered as part of a transnational education (TNE) program. In the light of the results, recommendations are made to improve the assessment and feedback activities in disruptive circumstances

Compressive sensing theory for low power wireless EEG communication systems

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