Implementation of Health Care Monitoring System using low power MCU’s and ARM CORTEX A8

The present common goal in the medical information technology is the design and implementation of telemedicine solutions, which provides a reliable and quality of services to patients. With the advent of recent development in sensors, low-power integrated circuits (IC’s), and wireless communications have brought the design of miniature, low-cost, and intelligent body sensor modules. These modules are capable of measuring, processing, communicating one or more physiological parameters, and can be integrated into a wireless personal area network. In this paper, we proposed a wireless body sensor module, based on low power microcontrollers and RF devices that perform the measurements and transmit the different bio sensors data to a Local Sensor Network server. Local Sensor Network (LSN) server will run a signal monitor application which  receives the information from wireless sensor module and draw the signal graph on the display according to received data and further updated to central health care surveillance centre. The LSN server should be able to connect all the nearby sensor modules through wireless media and update its data periodically.  Any sudden urge found in the signal will alarm the corresponding doctor. In order to handle such more number of sensor module connections, the Local sensor network server should be implemented with high performance processor. In this paper, the ARM Cortex A8 processor is one of the best choices to meet all the requirements o

Design of a smart security registration plate for seagoing motorized non-mechanical crafts.

Unauthorized and uncertified small seagoing fishing crafts are being widely used for illegal, unreported and unregulated fishing, especially in the Indian sub-continent. Nevertheless, the registration is mandatory; the display of the registration mark and its standards for the seagoing fishing craft has not been specified in detail. Now, any fraudster can mark a fake registration number quite easily on the craft and can be used for criminal activities in the coastal areas because of the effortless wipe out possibility for the present registration marks. Thus due to the lack of standardization and regulation for the display of registration number plate for the seagoing craft, identification of genuine one is very difficult. This paper proposes a typical design of a Smart Security Registration Plate (SSRP) using Radio Frequency Identification (RFID) technology to provide secure authentication of the motorized non-mechanical fishing crafts

Assessment of Safety and Interference Issues of Radio Frequency Identification Devices in 0.3 Tesla Magnetic Resonance Imaging and Computed Tomography

The objective of this study was to evaluate two issues regarding magnetic resonance imaging (MRI) including device functionality and image artifacts for the presence of radio frequency identification devices (RFID) in association with 0.3 Tesla at 12.7 MHz MRI and computed tomography (CT) scanning. Fifteen samples of RFID tags with two different sizes (wristband and ID card types) were tested. The tags were exposed to several MR-imaging conditions during MRI examination and X-rays of CT scan. Throughout the test, the tags were oriented in three different directions (axial, coronal, and sagittal) relative to MRI system in order to cover all possible situations with respect to the patient undergoing MRI and CT scanning, wearing a RFID tag on wrist. We observed that the tags did not sustain physical damage with their functionality remaining unaffected even after MRI and CT scanning, and there was no alternation in previously stored data as well. In addition, no evidence of either signal loss or artifact was seen in the acquired MR and CT images. Therefore, we can conclude that the use of this passive RFID tag is safe for a patient undergoing MRI at 0.3 T/12.7 MHz and CT Scanning

Hadoop-Based Intelligent Care System (HICS) : Analytical Approach for Big Data in IoT

The Internet of Things (IoT) is increasingly becoming a worldwide network of interconnected things that are uniquely addressable, via standard communication protocols. The use of IoT for continuous monitoring of public health is being rapidly adopted by various countries while generating a massive volume of heterogeneous, multisource, dynamic, and sparse high-velocity data. Handling such an enormous amount of high-speed medical data while integrating, collecting, processing, analyzing, and extracting knowledge constitutes a challenging task. On the other hand, most of the existing IoT devices do not cooperate with one another by using the same medium of communication. For this reason, it is a challenging task to develop healthcare applications for IoT that fulfill all user needs through real-Time monitoring of health parameters. Therefore, to address such issues, this article proposed a Hadoop-based intelligent care system (HICS) that demonstrates IoT-based collaborative contextual Big Data sharing among all of the devices in a healthcare system. In particular, the proposed system involves a network architecture with enhanced processing features for data collection generated by millions of connected devices. In the proposed system, various sensors, such as wearable devices, are attached to the human body and measure health parameters and transmit them to a primary mobile device (PMD). The collected data are then forwarded to intelligent building (IB) using the Internet where the data are thoroughly analyzed to identify abnormal and serious health conditions. Intelligent building consists of (1) a Big Data collection unit (used for data collection, filtration, and load balancing); (2) a Hadoop processing unit (HPU) (composed of Hadoop distributed file system (HDFS) and MapReduce); and (3) an analysis and decision unit. The HPU, analysis, and decision unit are equipped with a medical expert system, which reads the sensor data and performs actions in the case of an emergency situation. To demonstrate the feasibility and efficiency of the proposed system, we use publicly available medical sensory datasets and real-Time sensor traffic while identifying the serious health conditions of patients by using thresholds, statistical methods, and machine-learning techniques. The results show that the proposed system is very efficient and able to process high-speed WBAN sensory data in real time

Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date

The Sensor Systems Design and Optimization for Energy Harvesting Applications

Disertační práce se zabývá problematikou zpracování energie z alternativních zdrojů pomocí tzv. "Energy harvestingu". Řeší aktuální problém s napájením autonomních senzorických sítí, kdy u těžko dostupných zařízení nelze provádět výměnu primárního napájecího zdroje. V takových případech je třeba získávat energii z vnějších zdrojů, např. tepla, světla, pohybu apod. Navrhované řešení nachází uplatnění především v oblasti medicínských aplikací, zvláště u implantovaných zařízení (kochleární implantáty, kardiostimulátory, inzulinové pumpy atd.), ale také u systémů pro dlouhodobé monitorování zdravotního stavu pacienta. V práci byl zpracován aktuální stav problematiky nízkopříkonových senzorických systémů a přehled aktuálních možností alternativního napájení v souvislosti s těmito systémy. Z rešerše vyplynulo, že současné senzorické systémy jsou téměř připraveny pro implementaci malovýkonových zdrojů čerpajících energii z okolního prostředí ("Energy harvesting" zdrojů). Ty jsou vyvíjeny mnoha zahraničními vědeckými týmy, avšak existuje pouze několik variant zapojení následných obvodů pro efektivní čerpání, uchování a zpracování energie z těchto zdrojů, což nabízí široký prostor pro další možný výzkum. Problematika redistribuce energie z malovýkonových generátorů je v práci řešena na komplexní úrovni. Pro tyto účely byl vypracován a diskutován nový simulační model vybraného rozsáhlého senzorického systému (plně implantovatelné umělé kochley) v prostředí SPICE. S jeho využitím bylo navrženo několik nových přístupů pro řešení napájení (především technika protlačení náboje, tzv. "Charge Push Through"), které jsou aplikovatelné na většinu obecných typů senzorických systémů. Díky tomu bude v budoucnu umožněna praktická realizace senzorických systémů využívajících energii čerpanou z okolního prostředí.Dissertation thesis is focused on using alternative energy sources called energy harvesting. This thesis offers a solution to problems with autonomous powering of sensor networks if primary power source recovery is impossible. In these cases, energy of the external power (e.g. temperature, light, motion) should be used. Proposed solution should be especially used in the field of medical applications (e.g. cochlear implants, pacemakers, insulin pumps). Long time monitoring of the personal health status is also possible when employing automated sensor systems. In this work, there is state of art review relating to the low power energy sources for an alternative powering of sensor systems. It was observed that existing systems are almost prepared for the implementation of energy harvesting power sources. The energy harvesting power sources have been developed by numerous researcher teams around the world, but there are only a few variants of power management circuits for effective energy gaining, storing and using. This area has a huge potential for the next research. The issues regarding to the distribution of gained energy are solved on the complex level in the thesis. For these purposes, a new simulation model of the whole system (fully implantable artificial cochlea) including its subcircuits was developed in the SPICE environment. It connects independent subcircuits into a single comprehensive model. Using this model, a few novel principles for energy distribution (e.g. Charge Push Through technique) was developed. In the near future, these techniques are also applicable to the design of versatile sensor systems.

CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply

A Low-Power RFID Integrated Circuits for Intelligent Healthcare Systems

This paper presents low-power radio-frequency identification (RFID) technology for intelligent healthcare systems. With attention to power-efficient communication in the body sensor network, RF power transfer was estimated and the required lowpower ICs, which are important in the development of a healthcare system with miniaturization and system integration, are discussed based on the RFID platform. To analyze the power transformation, this paper adopts a 915-MHz industrial, scientific, and medical RF with a radiation power of 70 mW to estimate the power loss under the 1-m communication distance between an RFID reader (bioinformation node) and a transponder (biosignal acquisition nodes). The low-power ICs of the transponder will be implemented in the TSMC 0.18-µm CMOS process. The simulation result reveals that the transponder's IC can fit in with the link budget of the UHF RFID system