Body sensor networks for ubiquitous healthcare

Body sensor networks provide a platform for ubiquitous healthcare, driving the diagnosis in hospital static environment to the daily life dynamic context. We realized the importance of sensing of activities, which is not only a dimension of human health but also important context information for diagnosis based on the physiologic data. This paper presents our ubiquitous healthcare system, uCare. It consists of uCare devices and a server system. Currently, the uCare system is designed for cardiovascular disease (CVD) examination and management. The uCare device has been tested in a trial in Beijing Hospital. The uCare system will be further tested in elderly care at home and exercise management in training to measure heart dynamics during training.

TuLP: A Family of Lightweight Message Authentication Codes for Body Sensor Networks

A wireless sensor network （WSN） commonly whilst a body sensor network （BSN） must be secured with requires lower level security for public information gathering, strong authenticity to protect personal health information. In this paper, some practical problems with the message authentication codes （MACs）, which were proposed in the popular security architectures for WSNs, are reconsidered. The analysis shows that the recommended MACs for WSNs, e.g., CBC- MAC （TinySec）, OCB-MAC （MiniSec）, and XCBC-MAC （SenSee）, might not be exactly suitable for BSNs. Particularly an existential forgery attack is elaborated on XCBC-MAC. Considering the hardware limitations of BSNs, we propose a new family of tunable lightweight MAC based on the PRESENT block cipher. The first scheme, which is named TukP, is a new lightweight MAC with 64-bit output range. The second scheme, which is named TuLP-128, is a 128-bit variant which provides a higher resistance against internal collisions. Compared with the existing schemes, our lightweight MACs are both time and resource efficient on hardware-constrained devices.

Smart Body Sensor Object Networking

This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

An Overview of the Miniaturization and Endurance for Wearable Devices

The miniaturization and endurance of wearable devices have been the research direction for a long time.With the development of nanotechnology and the emergence of microelectronics products,people have explored many new strategies that may be applied to wearable devices.In this overview,we will summarize the recent research of wearable devices in these two directions,and summarize some available related technologies.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people

This paper presents a sensor-guided gait-synchronization system to help potential unilateral knee-injured people walk normally with a weight-supported lower-extremity-exoskeleton(LEE).This relieves the body weight loading on the knee-injured leg and synchronizes its motion with that of the healthy leg during the swing phase of walking.The sensor-guided gait-synchronization system is integrated with a body sensor network designed to sense the motion/gait of the healthy leg.Guided by the measured joint-angle trajectories,the motorized hip joint lifts the links during walking and synchronizes the knee-injured gait with the healthy gait by a half-cycle delay.The effectiveness of the LEE is illustrated experimentally.We compare the measured joint-angle trajectories between the healthy and knee-injured legs,the simulated knee forces,and the human-exoskeleton interaction forces.The results indicate that the motorized hip-controlled LEE can synchronize the motion/gait of the combined body-weight-supported LEE and injured leg with that of the healthy leg.