Continuous medical monitoring using wireless microsensors

Shih, Eugene; Bychkovsky, Vladimir; Curtis, Dorothy; Guttag, John V.

doi:10.1145/1031495.1031553

Cited by 12 publications

(3 citation statements)

References 2 publications

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“…The communication among nodes is done in a wireless fashion, and thus, the name of wireless sensor networks. One of the most promising applications is e-health using WSNs [2,3].…”

Section: Introductionmentioning

confidence: 99%

A lightweight middleware for an e-health WSN based system using Android technology

Boulmalf

Belgana

Sadiki

et al. 2012

2012 International Conference on Multimedia Computing and Systems

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With the rapid development of Wireless Sensors Network (WSN) technology, together with its growing market due to the variety of applications in which this technology can be used, sensors are now available at very cheap prices, and relatively high quality. The medical field among other fields is one of the most promising fields in which sensors technology is undergoing great improvements. With the constant and continuing advances on the wireless sensor networks and applications, lots of medical applications that use sensors are emerging. Varying from pre-hospital, in-hospital applications to post-hospital and rehabilitation applications, sensors are used in a wide range of applications such as patient's drug administration to elderly remote supervision. With the lake of standardization, a framework that would govern the interaction between the applications and the network is a must to be capable of sensing, processing, and communicating vital health signs. In this research we proposed layered approach architecture for a lightweight portable middleware with the Android technology.Our system takes into consideration the design principles and issues related to medical applications. A prototype of the lightweight middleware for an e-Health WSN based System was designed and tested and we showed that the results are very promising.

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“…The communication among nodes is done in a wireless fashion, and thus, the name of wireless sensor networks. One of the most promising applications is e-health using WSNs [2,3].…”

Section: Introductionmentioning

confidence: 99%

A lightweight middleware for an e-health WSN based system using Android technology

Boulmalf

Belgana

Sadiki

et al. 2012

2012 International Conference on Multimedia Computing and Systems

View full text Add to dashboard Cite

show abstract

“…Several wireless ECG monitoring systems have been proposed [1], [2], [3], [4], [5], [6]. All of them use conventional "wet" ECG sensors.…”

Section: Introductionmentioning

confidence: 99%

An ultra-wearable, wireless, low power ECG monitoring system

Park

Chou

Bai

et al. 2006

2006 IEEE Biomedical Circuits and Systems Conference

212

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Abstract-Wearable electrocardiograph (ECG) monitoring systems today use electrodes that require skin preparation in advance, and require pastes or gels to make electrical contact to the skin. Moreover, they are not suitable for subjects at high levels of activity due to high noise spikes that can appear in the data. To address these problems, a new class of miniature, ultra low noise, capacitive sensor that does not require direct contact to the skin, and has comparable performance to gold standard ECG electrodes, has been developed. This paper presents a description and evaluation of a wireless version of a system based on these innovative ECG sensors. We use a wearable and ultra low power wireless sensor node called Eco. Experimental results show that the wireless interface will add minimal size and weight to the system while providing reliable, untethered operation.

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“…Research prototypes [2,3] and commercial products [4] have successfully shown the potential for monitoring physiological states of patients with wearable physiological sensors.…”

Section: Introductionmentioning

confidence: 99%

Label Disambiguation and Sequence Modeling for Identifying Human Activities from Wearable Physiological Sensors

Lin

Hauptmann

2006

2006 IEEE International Conference on Multimedia and Expo

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Wearable physiological sensors can provide a faithful record of a patient's physiological states without constant attention of caregivers. A computer program that can infer human activities from physiological recordings will be an valuable tool for physicians. In this paper we investigate to what extent current machine learning algorithms can correctly identify human activities from physiological sensors. We further identify two challenges that developers need to address. The first problem is that the labels of training data are inevitably noisy due to difficulties of annotating thousands hours of data. The second problem lies in the continuous nature of human activities, which violates the independence assumption made by many learning algorithms. We approach the first problem of noisy labeling in the multiple-label framework, and develop a conditional Markov Models to take temporal context into consideration. We evaluate the proposed methods on 12,000 hours of the physiological recordings. The results show that Support Vector Machines are effective to identify human activities from physiological signals, and efforts of disambiguating noisy labels are worthwhile.

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Continuous medical monitoring using wireless microsensors

Cited by 12 publications

References 2 publications

A lightweight middleware for an e-health WSN based system using Android technology

A lightweight middleware for an e-health WSN based system using Android technology

An ultra-wearable, wireless, low power ECG monitoring system

Label Disambiguation and Sequence Modeling for Identifying Human Activities from Wearable Physiological Sensors

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