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ABSTRACT: This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver's capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring.
Sensors 01/2012; 12(12):17536-52. · 1.74 Impact Factor
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ABSTRACT: This paper presents the designing of a neural network for the classification
of Human activity. A Triaxial accelerometer sensor, housed in a chest worn
sensor unit, has been used for capturing the acceleration of the movements
associated. All the three axis acceleration data were collected at a base
station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant),
processed and classified using MATLAB. A neural network approach for
classification was used with an eye on theoretical and empirical facts. The
work shows a detailed description of the designing steps for the classification
of human body acceleration data. A 4-layer back propagation neural network,
with Levenberg-marquardt algorithm for training, showed best performance among
the other neural network training algorithms.
07/2011;
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ABSTRACT: The development of wireless sensor networks has greatly encouraged the use of sensors in the location tracking industry by introducing various researches have exploited the high accuracy of position determination using distinct tracking models predominantly as low-cost solutions. This paper presents an indoor tracking model that uses the depreciated IEEE 802.15.4 compliant radio frequency and immersive 3-D graphics to present data in an extraordinary way. Our motivation is to manipulate the erratic and unstable received signal strength indicator (RSSI) signal to deliver stable and precise position information in an indoor environment. A sensor (target node) receives RSSI signals from at least three designated sensors (reference nodes) and predicts the location on the basis of trilateration. The nature of a limited indoor space is such that there is signal fluctuation and noise in radio-frequency transmission between sensors. In light of this issue, we proposed an accuracy refinement algorithm to filter out noise in the received RSSI signals. To display useful 3-D information, an additional feature is required in conjunction with the user's position to illustrate the view of the user. Therefore, a digital magnetic compass is used to determine the orientation of the target in real- time and a sensor transmits DMC data to a receiver in wireless transmission.
IEEE Sensors Journal 04/2011; · 1.52 Impact Factor
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ABSTRACT: Wireless healthcare monitoring in a sensor network has different requirements and priorities from environmental, agricultural and industrial monitoring. This paper discusses some of the software design issues that must be addressed when implementing query-driven healthcare monitoring, as well as the effects that the varying sampling rates and packet sizes required by different medical sensors have on network performance. Some existing MAC (media access control) and network layer protocols are modified to enhance their compatibility and flexibility for use in healthcare systems.
Sensor Letters 01/2011; 9(1):384-392. · 0.82 Impact Factor
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ABSTRACT: Provision of ubiquitous healthcare solutions which provide healthcare services at anytime anywhere has become more favorable nowadays due to the emphasis on healthcare awareness and also the growth of mobile wireless technologies. Following this approach, an Android™ smart phone device is proposed as a mobile monitoring terminal to observe and analyze ECG (electrocardiography) waveforms from wearable ECG devices in real time under the coverage of a wireless sensor network (WSN). The exploitation of WSN in healthcare is able to substitute the complicated wired technology, moving healthcare away from a fixed location setting. As an extension to the monitoring scheme, medicine care is taken into consideration by utilizing the mobile phone as a barcode decoder, to verify and assist out-patients in the medication administration process, providing a better and more comprehensive healthcare service.
Sensors 01/2011; 11(7):6799-815. · 1.74 Impact Factor
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ABSTRACT: Driver's drowsiness and fatigue have been major causes of the serious traffic accidents, which make this an area of great socioeconomic concern. This paper describes the design of ECG (Electrocardiogram) sensor with conductive fabric electrodes and PPG (Photoplethysmogram) sensor to obtain physiological signals for car driver's health condition monitoring. ECG and PPG signals are transmitted to base station connected to the server PC via personal area network for practical test. Intelligent health condition monitoring system is designed at the server to analyze the PPG and ECG signals. Our purpose for intelligent health condition monitoring system is managed to process HRV signals analysis derived from the physiological signals in time and frequency domain and to evaluate the driver's drowsiness status.
Sensors, 2010 IEEE; 12/2010
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IJHISI. 01/2010; 5:30-36.
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ABSTRACT: Due to recent development in Ubiquitous Healthcare now it’s time to build such application which can work independently and
with less interference of Physician. In this paper we are try to build the whole architecture of personal Healthcare information
system for ubiquitous healthcare which also included Middleware, existing between application GUI and data source. To build
this application we studied several existing application and ongoing projects and tried to build an architecture which can
be appropriate to handle necessary information related to healthcare of individuals. We already succeed to design a prototype
of Ubiquitous Healthcare Data Analysis and Monitoring Using Multiple Wireless Sensors for Elderly Person at home. Our proposed
architecture will also provide diagnoses report to the doctors for further instructions. The diagnoses report will consist
of healthcare data analysis results and history of patient. We have considered healthcare data like ECG, Accelerometer and
temperature for experiment, which are important as a basic health need.
11/2009: pages 157-164;
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ABSTRACT: An advanced ubiquitous healthcare monitoring system is proposed with the aid of wireless sensor network based on the IEEE 802.15.4 standard relay sensor nodes. In this work, wearable healthcare devices are used to measure the human body vital signs, while relay sensor nodes are installed at the room's ceiling to transfer the patient's vital signs to the desktop or mobile. Our proposed architecture had utilized the advantages of wireless sensor network where stationary relay sensor nodes installed at the ceiling manage to transfer an amount of vital signs in real time to the server PC through the connected gateway by concerning the signals traffic problem in mind. Therefore, the proposed system had deal with the mobility and transmission reliability issues faced by the healthcare system. Old-fashionable healthcare system that consumes a lot of power during transmission had been replaced with low power consumption relay sensor nodes with wider communication zone available.
Sensors, 2009 IEEE; 11/2009
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ABSTRACT: This paper presents bilateral key handshaking method between patient and medical people. It builds using pseudo inverse matrix investigates to attack an adaptation layer of IP-USNs (IP-base ubiquitous sensor network). It is a healthcare facilities technique among medical people and patient to convey biomedical data accurately and securely. However, the implementation presents a secure global healthcare monitoring system can be utilized and can consequently improve service providers' workflow. Their lots of system already implemented for security such as large databases, complex expert and knowledge-based systems. But in healthcare monitoring case, patient privacy is a very sensitive and important issue. However, anywhere at any time connectivity between patient and service provider technique is designed.
Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the; 09/2009
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ABSTRACT: A biomedical signals monitoring system with multiple physiological signs measurement capability in real time is designed and implemented. This system performs a local vital sign data analysis using cell phone over wireless sensor network (WSN) technology and code division multiple access (CDMA) network. Cell phone-based personal medical recorder becomes popular to facilitate self-control of chronic diseases and observe patients’ condition continuously. The risk of ignoring monitoring patients’ physiological data by doctor is concerned since large quantities of data are processed and analyzed daily. An intelligent decision support system is developed to enhance healthcare providers’ review of monitoring data.
Electronic Commerce and Security, International Symposium. 05/2009; 1:605-608.
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ABSTRACT: The link layer routing protocols 6lowpan ad hoc on-demand distance vector routing (LOAD) and dynamic MANET on-demand for 6lowpan (DYMO-low) are integrated in 6lowpan stack. The stack is less complicated and it support IPv6 enabled packets route for biomedical data. Both routing protocols are modified and developed new approach for patient healthcare monitoring system. Thus, proposed integration method is more reliable speedily in healthcare monitoring system than existing protocol in 6lowpans. Mesh routing components are introduced to enable messages to deliver around the nodes. Itpsilas a new approach to monitor patients globally and analysis healthcare biomedical data, even though the doctors and patients both are fart in distance. During emergency cases, doctor able to monitor patient condition efficiently to reduce time consuming, thus is provider more effective healthcare system. For real-time testing, dummy biomedical data are used in IP-based ubiquitous sensor nodes and gateway. Real-time implementation results are show for the integration protocol and it is more reliable in compare to other 6lowpan protocols.
Computer Science and Information Engineering, 2009 WRI World Congress on; 05/2009
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ABSTRACT: We build MUHIS (Middleware for Ubiquitous Healthcare Information System) architecture with LiveGraph, J2EE and Oracle to process different ubiquitous healthcare application's data and process that data into useful information, which can play a important role in decision making in ubiquitous healthcare system. Application developers mostly rely on third party middleware, tools and libraries (i.e., Web servers, distributed middleware such as CORBA, XML etc.) to respond the emerging trends of their target domain. With this middleware we tried to enhance the efficiency of application by decrease their memory uses, data processing and decision making on another Web module which is independent of each application. In our middleware system, we also proposed an application by which we can find some important conclusion about different health status likewise ECG, accelerometer. etc., which can be used in various data processing and determine the current health status. In this paper we also analyze some different low level and high level middleware technology which were used to build different kind middleware likewise CAMUS, MiLAN and try to find the best solution in the form of middleware for ubiquitous healthcare information system. T build our middleware we also analyze different application such as Matlab, Oscilloscope, and JFreeChart and try to extract most common feature, and then try to build a advance LiveGraph which consist most of the common feature of above and also have some advance feature which is most feasible to analyze the data and to take some decision itself.
Multimedia, Signal Processing and Communication Technologies, 2009. IMPACT '09. International; 04/2009
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ABSTRACT: An efficient method to extract noiseless Electrocardiogram (ECG) signal which is utilized for diagnostics purpose is presented. An adaptive neuro-fuzzy filtering which is basically a nonlinear system structure presented here for the noise cancellation of biomedical signals (like ECG, PPG etc) measured by ubiquitous wearable sensor node (USN node). This paper presents non-linear adaptive filter which uses fuzzy neural network (FNN) to treat with the unknown noise and artifacts present in biomedical signals. The presented work based on ANFF (Adaptive Neuro Fuzzy Filter), where adaptation process includes neural network learning ability and fuzzy if-then rules with the optimal weight setting ability. ANFF is basically a fuzzy filtering implemented in the framework of adaptive neural networks environment. ANFF setting parameters such as the training epochs, number of MFs for each input and output, type of MFs for each input and output, learning algorithm etc. Finally simulated experimental results are presented for proper validation.
Advanced Communication Technology, 2009. ICACT 2009. 11th International Conference on; 03/2009
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ABSTRACT: Healthcare Information system requirements differ from other existing applications. The gap between existing application and medical care needs makes it more complex. To get the maximum benefit of ubiquitous healthcare system it is necessary to build a middleware between different ubiquitous applications which can play an important role in management of data and information processing. To enable medical personal for real-time triage, correlation with hospital records and long-term observation of patients is necessary, while sharing the most accurate and real-time patient information. The technology requirements of various domains are increasing, such as distributed platforms are commonly in use, the quality of service requirements for ubiquitous healthcare systems are increasing as the low level sensor network grows powerful, customers ask for platform independent software and interoperability, etc. middleware is a class of software technologies designed to help manage the complexity and heterogeneity inherent in distributed systems. It is defined as a layer of software above the operating system but below the application program that provides a common programming abstraction across a distributed system. Middleware can be classifying into two levels, low level middleware and high level middleware. Low level middleware exist between different sensor node, raw data and their application. High level middleware exist between different application which is used for exchange data and communication between various applications. If we first take the consideration then we find there is great work has been done on low level middleware and their different technology. Like Mobiware, ALICE, CAMUS, MiLAN etc. but if we talk about high level middleware then we find there is not good work has been done. And we find that without the proper research of this field we can not achieve the real ubiquitous healthcare system. In this paper, we present a review of the low level and high lev-
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el middleware literature and then enumerate these common features and then we briefly presents our own middleware system and how it relates to these features.
Advanced Communication Technology, 2009. ICACT 2009. 11th International Conference on; 03/2009
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ABSTRACT: Recent, technological advances in healthcare monitoring, sensors and wireless networking able to design a system for global healthcare monitoring applications such as ECG, SpO<sub>2</sub>, glucose, temperature etc. A 6lowpan (IPv6 low power wireless personal area networks) node with biomedical sensors strategically placed on the patient body area networks that can monitor biomedical data. The 6lowpan node has IP-address so it provides real-time feedback of patient to the service provider or doctor. Patient freely moves in-side the PAN or hospital area and service provider efficiently able to receive biomedical data. This technique we modified AODV protocol for pervasive 6lowpan networks. It used high level computational multi-hop communication method for transmitting biomedical data to the gateway. NS-2.33 simulator result shows the modified AODV protocol has better performance then LOAD and DYMO-low routing protocols for healthcare parameters. We tested real-time our modified protocols and show the performance of packet delivery ratio in time interval between 6lowpan nodes for healthcare parameters.
Advanced Communication Technology, 2009. ICACT 2009. 11th International Conference on; 03/2009
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TIIS. 01/2009; 3:108-118.
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CSIE 2009, 2009 WRI World Congress on Computer Science and Information Engineering, March 31 - April 2, 2009, Los Angeles, California, USA, 7 Volumes; 01/2009
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Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human 2009, Seoul, Korea, 24-26 November 2009; 01/2009
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Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human 2009, Seoul, Korea, 24-26 November 2009; 01/2009
