SVM based context awareness using body area sensor network for pervasive healthcare monitoring

2010 ◽  
Author(s):  
Sonali Agarwal ◽  
Divya ◽  
G. N. Pandey
Keyword(s):  
Sensor Network ◽  
Context Awareness ◽  
Body Area ◽  
Pervasive Healthcare ◽  
Healthcare Monitoring

Architecture of a Wireless Wearable Body Area Sensor Network for Work Risk Assessment

2021 ◽  
Author(s):  
Stefano di Modica ◽  
Marco di Rienzo ◽  
Fabio di Francesco ◽  
Enzo Pasquale Scilingo ◽  
Antonio Lanata
Keyword(s):  
Risk Assessment ◽  
Sensor Network ◽  
Body Area

scholarly journals Deploying Sensors for Gravity Measurement in a Body-Area Inertial Sensor Network

2013 ◽  
Vol 13 (5) ◽  
pp. 1522-1533 ◽  
Author(s):  
Chun-Hao Wu ◽  
Yu-Chee Tseng
Keyword(s):  
Sensor Network ◽  
Inertial Sensor ◽  
Gravity Measurement ◽  
Body Area

scholarly journals CONTEXT STORAGE USING CLOUD-BASED MONGODB WITH RULE-BASED RETE ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 1022-1030
Author(s):  
Shivakumar. C, Et. al.
Keyword(s):  
Sensor Network ◽  
Context Awareness ◽  
Research Work ◽  
Context Aware ◽  
The Novel ◽  
Rule Based ◽  
Smart System ◽  
Context Aware Computing ◽  
Context Data ◽  
Day By Day

In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day.  The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..                       

System Design and Data Fusion in Body Sensor Networks

2012 ◽  
pp. 1-25 ◽  
Author(s):  
Kai Lin ◽  
Min Chen ◽  
Joel J. P. C. Rodrigues ◽  
Hongwei Ge
Keyword(s):  
Sensor Networks ◽  
Data Fusion ◽  
System Design ◽  
Sensor Network ◽  
Evaluation Method ◽  
Data Evaluation ◽  
Body Sensor Networks ◽  
Sensory Data ◽  
Redundant Data ◽  
Healthcare Monitoring

Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.

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