A Review of Wireless Sensor Networks for Wellness Monitoring in Residential Aged Care

2009 ◽  
Vol 1 (4) ◽  
pp. 32-47
Author(s):  
Leroy Lai Yu Chan ◽  
Branko George Celler ◽  
James Zhaonan Zhang ◽  
Nigel Hamilton Lovell
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Vital Signs ◽  
Healthcare Services ◽  
Developed Countries ◽  
Aged Care ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Residential Aged Care ◽  
Enabling Technologies

With the increasing shift in the population profile to the older demographic and rising healthcare costs, it is more critical for developed countries to deliver long-term and financially sustainable healthcare services, especially in the area of residential aged care. A consensus exists that innovations in the area of Wireless Sensor Networks (WSNs) are key enabling technologies for reaching this goal. The major focus of this article is WSN design considerations for ubiquitous wellness monitoring systems in residential aged care facilities. Major enabling technologies for building a pervasive WSN will be detailed, including descriptions on sensor design, wireless communication protocols and network topologies. Also examined are data processing methods and knowledge management tools to support the collection of sensor data and their subsequent analysis for health assessment. To introduce future healthcare reform in residential aged care, two aspects of wellness monitoring, vital signs and activities of daily living (ADL) monitoring, will be discussed.

A Review of Wireless Sensor Networks for Wellness Monitoring in Residential Aged Care

2011 ◽  
pp. 294-310
Author(s):  
Leroy Lai Yu Chan ◽  
Branko George Celler ◽  
James Zhaonan Zhang ◽  
Nigel Hamilton Lovell
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Healthcare Services ◽  
Aged Care ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Residential Aged Care ◽  
Enabling Technologies ◽  
Aged Care Facilities ◽  
Key Enabling Technologies

With the increasing shift in the population profile to the older demographic and rising healthcare costs, it is more critical for developed countries to deliver long-term and financially sustainable healthcare services, especially in the area of residential aged care. A consensus exists that innovations in the area of Wireless Sensor Networks (WSNs) are key enabling technologies for reaching this goal. The major focus of this article is WSN design considerations for ubiquitous wellness monitoring systems in residential aged care facilities. Major enabling technologies for building a pervasive WSN will be detailed, including descriptions on sensor design, wireless communication protocols and network topologies. Also examined are data processing methods and knowledge management tools to support the collection of sensor data and their subsequent analysis for health assessment. To introduce future healthcare reform in residential aged care, two aspects of wellness monitoring, vital signs and activities of daily living (ADL) monitoring, will be discussed.

Pervasive Networks and Ubiquitous Monitoring for Wellness Monitoring in Residential Aged Care

2010 ◽  
pp. 1-24
Author(s):  
Leroy Lai Yu Chan ◽  
Branko George Celler ◽  
James Zhaonan Zhang ◽  
Nigel Hamilton Lovell
Keyword(s):  
Healthcare Services ◽  
Developed Countries ◽  
Aged Care ◽  
Sensor Data ◽  
Residential Aged Care ◽  
Management Tools ◽  
Enabling Technologies ◽  
Aged Care Facilities ◽  
Residential Aged Care Facilities ◽  
Key Enabling Technologies

It is becoming more critical for developed countries to deliver long-term and financially sustainable healthcare services to an expanding ageing population, especially in the area of residential aged care. There is a general consensus that innovations in the area of Wireless Sensor Networks (WSNs) are key enabling technologies for reaching this goal. The major focus of this chapter is on WSN design considerations for ubiquitous wellness monitoring systems in residential aged care facilities. The major enabling technologies for building a pervasive WSN will be explored, including details on sensor design, wireless communication protocols and network topologies. Also examined are various data processing methods and knowledge management tools to support the collection of sensor data and their subsequent analysis for health assessment. Future systems that incorporate the two aspects of wellness monitoring, vital signs and activities of daily living (ADL) monitoring, will also be introduced.

Implementation of a Cyber-Physical System Using Wireless Sensor Networks for Monitoring Patients

2015 ◽  
Vol 1 ◽  
pp. 26 ◽  
Author(s):  
Vo Que Son ◽  
Do Tan A
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Physical System ◽  
Patient Monitoring ◽  
High Reliability ◽  
Vital Signs ◽  
Wireless Sensor ◽  
Cyber Physical System ◽  
Self Administration ◽  
Hospital Units

Sensing, distributed computation and wireless communication are the essential building components of a Cyber-Physical System (CPS). Having many advantages such as mobility, low power, multi-hop routing, low latency, self-administration, utonomous data acquisition, and fault tolerance, Wireless Sensor Networks (WSNs) have gone beyond the scope of monitoring the environment and can be a way to support CPS. This paper presents the design, deployment, and empirical study of an eHealth system, which can remotely monitor vital signs from patients such as body temperature, blood pressure, SPO2, and heart rate. The primary contribution of this paper is the measurements of the proposed eHealth device that assesses the feasibility of WSNs for patient monitoring in hospitals in two aspects of communication and clinical sensing. Moreover, both simulation and experiment are used to investigate the performance of the design in many aspects such as networking reliability, sensing reliability, or end-to-end delay. The results show that the network achieved high reliability - nearly 97% while the sensing reliability of the vital signs can be obtained at approximately 98%. This indicates the feasibility and promise of using WSNs for continuous patient monitoring and clinical worsening detection in general hospital units.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1782
Author(s):  
Yulong Deng ◽  
Chong Han ◽  
Jian Guo ◽  
Lijuan Sun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Missing Data ◽  
Nearest Neighbor ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Temporal And Spatial ◽  
Two Parameters

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

A Novel Method for Data Calibration in Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1068-1073
Author(s):  
Wei Min Qi ◽  
Jie Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Errors ◽  
Information Technologies ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Data ◽  
Error Elimination ◽  
Calibration Algorithm ◽  
Data Calibration

In order to provide efficient and suitable services for users in a ubiquitous computing environment, many kinds of context information technologies have been researched. Wireless sensor networks are among the most popular technologies providing such information. Therefore, it is very important to guarantee the reliability of sensor data gathered from wireless sensor networks. However there are several factors associated with faulty sensor readings which make sensor readings unreliable. The research put forward classifying faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data calibration algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings and increase reliability. The simulation study shows that the in-network data calibration algorithm is highly reliable and its network overhead is very low compared to previous works.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

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