A 3G/WiFi-enabled 6LoWPAN-based u-healthcare system for ubiquitous real-time monitoring and data logging

Ubiquitous healthcare (U-healthcare) systems are expected to offer flexible and resilient high-end technological solutions enabling remote monitoring of patients health status in real-time and provisioning of feedback and remote actions by healthcare providers. In this paper, we present a 6LowPAN based U-healthcare platform that contributes to the realization of the above expectation. The proposed system comprises two sensor nodes sending temperature data and ECG signals to a remote processing unit. These sensors are being assigned an IPv6 address to enable the Internet-of-Things (IoT) functionality. A 6LowPAN-enabled edge router, connected to a PC, is serving as a base station through a serial interface, to collect data from the sensor nodes. Furthermore, a program interfacing through a Serial-Line-Internet-Protocol (SLIP) and running on the PC provides a network interface that receives IPv6 packets from the edge router. The above system is enhanced by having the application save readings from the sensors into a file that can be downloaded by a remote server using a free Cloud service such as UbuntuOne. This enhancement makes the system robust against data loss especially for outdoor healthcare services, where the 3G/4G connectivity may get lost because of signal quality fluctuations. The system provided a proof of concept of successful remote U-healthcare monitoring illustrating the IoT functionality and involving 3G/4G connectivity while being enhanced by a cloud-based backup

Wavelet-based encoding scheme for controlling size of compressed ECG segments in telecardiology systems

One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption

Embedded gateway services for Internet of Things applications in ubiquitous healthcare

The continuous advancement in computer and communication technologies has made personalized healthcare monitoring a rapidly growing area of interest. New features and services are envisaged, raising users' expectations in healthcare services. The emergence of Internet of Things brings people closer to connect the physical world to the Internet. In this paper, we present embedded services that are part of a ubiquitous healthcare system that allows automated and intelligent monitoring. The system uses IP connectivity and the Internet for end-to-end communication, from each 6LoWPAN sensor nodes to the web user interface on the Internet. The proposed algorithm in the Gateway performs multithreaded processing on the gathered medical signals for conversion to real data, feature extraction and wireless display. The user interface at the server allows users to access and view the medical data from mobile and portable devices. The ubiquitous system is exploring possibilities in connecting Internet with things and people for health services

Adaptive order statistics rational hybrid filters for multichannel image processing

A new adaptive multichannel filtering approach is introduced and analyzed in this paper. The technique is sim pier and more appropriate than traditional approaches that have been addressed by means of groupwise vector ordering information. These filters are a two-stage filters based on rational functions (RF using fuzzy transformations of the Euclidean and angular distances among the different vectors to adapt to local data in the color image. The output is the result of vector rational operation taking into account three fuzzy sub-function outputs. Simulation studies indicate that the filters are computationally attractive and have excellent performance such as edge and details preservation and accurate chromaticity estimation