Abnormality detection on vital parameters using modified weighted average method in cloud
01 Aug 2017-pp 63-68
TL;DR: A cloud based health monitoring system has been developed using a Modified Weighted Average Method (MWAM) along with Naïve Bayesian classifier and it is found that the MWAM boosts the performance of the Na naïve Bayesian algorithm in alert generation.
Abstract: Personal Health Record (PHR) is an online service model that holds patient's vital parameter data from the sensors worn by the patient. It allows patients to easily share their health information from any location with doctors. An android smartphone fetches vital signs of the patient from the sensors configured with the smartphone. The various vital signs are grouped together on a time to time basis as a record (i.e., PHR) and is then uploaded to a cloud storage through the smartphone. The PHRs may contain abnormal vital signs. An algorithm running on the private cloud constantly monitors the vital signs streamed from the smartphone to detect any abnormality in the patient's health condition. Existing algorithms omits the patient's health history while detecting abnormality. To overcome this existing issue, a cloud based health monitoring system has been developed using a Modified Weighted Average Method (MWAM) along with Naive Bayesian classifier is proposed. The proposed Cloud based Remote Monitoring System(CRMS) classifies whether the patient's health condition is normal or abnormal by giving equal preference to patient's health history and current vital signs and also predicts the degree of abnormality on a scale of 1 to 3 (low, medium and high). An android application has been developed for the patients that receives live data from the WBAN sensors and uploads to the cloud. Abnormality detection algorithm is deployed on the cloud setup that continuously monitors the patient's vital parameters and alerts the doctor and patient's caretakers when the patient's health is abnormal. From the experimental results, it is found that the MWAM boosts the performance of the Naive Bayesian algorithm in alert generation.
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TL;DR: A new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems that can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives is designed.
Abstract: The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks’ scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority.
9 citations
Cites methods from "Abnormality detection on vital para..."
...Furthermore, traditional methods to solve multi-objectives include the weighting method [18,19], the restraint method [20] and the linear programming method [21]....
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TL;DR: This paper proposes an anomalous data detection and isolation approach for mobile health care systems, called AUDIT, that detects inaccurate measurements in real time and distinguishes between faults or errors and health events.
6 citations
Cites background or methods or result from "Abnormality detection on vital para..."
...Furthermore, other studies such as Ahsanul et al. (2015), Salem, Liu, et al. (2013), Naseem et al. (2013), Salem, Guerassimov, et al. (2014, 2013), H. Zhang et al. (2015), and Shravan et al. (2017) do not scale well in the case of multidimensional data because of two issues....
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...On the other hand, multivariate anomaly detection approaches are classified according to their corresponding techniques to find classes: classification-based (Salem, Guerassimov, et al., 2013; Salem, Guerassimov, et al., 2014; Shravan et al., 2017; Zhang et al., 2015), clustering-based (Naseem et al....
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...…to their corresponding techniques to find classes: classification-based (Salem, Guerassimov, et al., 2013; Salem, Guerassimov, et al., 2014; Shravan et al., 2017; Zhang et al., 2015), clustering-based (Naseem et al., 2013; Elmougy et al., 2017), nearest neighbour-based (Salem, Liu, et al.,…...
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...Moreover, other approaches (Salem, Liu, et al., 2013; Shravan et al. 2017) consider only spatial correlations between physiological attributes without taking into account the temporal correlation, hence having a low detection accuracy and high computational complexity in terms of time and storage requirements that are not available in a constrained device (e....
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...Moreover, other approaches (Salem, Liu, et al., 2013; Shravan et al. 2017) consider only spatial correlations between physiological attributes without taking into account the temporal correlation, hence having a low detection accuracy and high computational complexity in terms of time and storage…...
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01 Jan 2020
TL;DR: The MEDIDRONE intends to provide the task of periodically collecting the vital signs of the people in the village as a paid work to medical practitioners, social workers, and unemployed people which could improve the economic status of the country.
Abstract: Health care is one of the constitutional rights of the people in India. Every resident of this country has the right to the highest attainable standard of health both physically and mentally. But, in India, the number of active Primary Health care Centers (PHCs) is very less in number, especially in the remote regions. Even in places where there are sufficient PHCs, there is a shortage of doctors, staffs, and medicine. Proper medication should be offered to people in rural areas in order to improve their health, thereby reducing the number of deaths. The trending technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Predictive Analytics have a key role in improving the medication and in providing preventive medical care. Therefore, exploiting these technologies to diagnose and monitor patients remotely using Body Area Network (BAN) devices and smartphones, the MEDIDRONE is proposed. The MEDIDRONE has been designed to provide on-time emergency services to the people in rural villages using drones. In addition, predictive models have been trained to provide people with insights from the data collected for their long-term welfare. The MEDIDRONE besides providing health insights and alerts to the people, also addresses the issue of unemployment in rural areas. The MEDIDRONE intends to provide the task of periodically collecting the vital signs of the people in the village as a paid work to medical practitioners, social workers, and unemployed people which could improve the economic status of the country.
3 citations
References
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TL;DR: A survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Heart Disease Prediction reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods are not performing well.
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573 citations
"Abnormality detection on vital para..." refers background or methods in this paper
...Naïve Bayesian classifier [4,5,6], in machine learning, belongs to the family of probabilistic classifiers based on Bayes’ theorem with assumption between features....
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...Medical data mining [4] has been helpful in exploring the hidden patterns in the data sets of the medical domain....
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TL;DR: The authors evaluated the functionality and utility of a selection of personal health records as a means of providing patients and providers with universal access to updated medical information.
269 citations
"Abnormality detection on vital para..." refers background in this paper
...Wireless Body Area Network (WBAN) is a wireless network of sensors that may be placed inside or outside the human body to monitor the patient’s health [1]....
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01 Oct 2006
TL;DR: The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings.
Abstract: The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members
250 citations
03 Nov 2012
TL;DR: This work implemented both centralized and distributed k-means clustering algorithm in network simulator and results show that distributed clustering is efficient than centralized clustering.
Abstract: —A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Clustering is a critical task in Wireless Sensor Networks for energy efficiency and network stability. Clustering through Central Processing Unit in wireless sensor networks is well known and in use for a long time. Presently clustering through distributed methods is being developed for dealing with the issues like network lifetime and energy. In our work, we implemented both centralized and distributed k-means clustering algorithm in network simulator. k-means is a prototype based algorithm that alternates between two major steps, assigning observations to clusters and computing cluster centers until a stopping criterion is satised. Simulation results are obtained and compared which show that distributed clustering is efficient than centralized clustering. Keywords- wireless sensor network; clustering; ns-2; k-means; network stability
158 citations
"Abnormality detection on vital para..." refers background in this paper
...al.[7] detects abnormality by considering vital sign reading available at a particular period of time leaving behind the patient’s past health history....
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23 Sep 2009
TL;DR: Intelligent Mobile Health Monitoring System (IMHMS) is presented, which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors.
Abstract: Health monitoring is repeatedly mentioned as one of the main application areas for Pervasive computing. Mobile Health Care is the integration of mobile computing and health monitoring. It is the application of mobile computing technologies for improving communication among patients, physicians, and other health care workers. As mobile devices have become an inseparable part of our life it can integrate health care more seamlessly to our everyday life. It enables the delivery of accurate medical information anytime anywhere by means of mobile devices. Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight and intelligent bio-sensor nodes. These nodes, capable of sensing, processing, and communicating one or more vital signs, can be seamlessly integrated into wireless personal or body area networks for mobile health monitoring. In this paper we present Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors.
114 citations