Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring

Xiao, Shuo; Dhamdhere, Ashay; Sivaraman, Vijay; Burdett, Alison

doi:10.1109/jsac.2009.090105

Cited by 178 publications

(153 citation statements)

References 26 publications

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“…The processing scheme introduced in this study enables the unobtrusive assessment of affect. In practice, this can be achieved through either sensor networks or wearable sensors; for example, as embedded in electronic textiles [106,110]. Both of these branches of engineering have flourished since the start of the current century.…”

Section: Resultsmentioning

confidence: 99%

“…Both of these branches of engineering have flourished since the start of the current century. One can only conclude that this technology is rapidly maturing [2,23,64,75] and, consequently, is applied in a variety of domains; for example, health monitoring [1,23,37,64,75,106,110]. While the underlying technology is becoming both more miniaturized and more robust [37,64,75], various probes have been introduced.…”

Section: Resultsmentioning

confidence: 99%

“…Until recently, this has prevented biosignals being incorporated into consumer products. However, with the progress of wearable computing and wireless sensing technologies in the last decade, this problem quickly vanishes [2,23,37,58,64,75,77,106,110]. In parallel, biosignal recording, even with a certain amount of obtrusiveness, is embraced by the general public in Western societies (e.g., real-time ECG processing to guide athletes).…”

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

“…Nowadays, not only speech and computer vision but also biosignals such as ECG can be applied completely unobtrusively [2,23,37,58,64,75,77,106,110]. Speech and biosignals are par excellence suitable for personalized and ubiquitous emotion-aware computing technology.…”

Section: The Five Issues Under Investigationmentioning

confidence: 99%

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Ubiquitous emotion-aware computing

Broek

2011

Pers Ubiquit Comput

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Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people's emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence-arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p \ .001) of the participants' experienced valence-arousal, with 88% for valence and 99% for arousal (ps \ .001). The six basic emotions could also be discriminated (p \ .001), although the explained variance was much lower: 18-20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people's emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

Section: The Five Issues Under Investigationmentioning

confidence: 99%

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Ubiquitous emotion-aware computing

Broek

2011

Pers Ubiquit Comput

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“…The mobility of the user causes variations in the separation and orientation of the antennas of the nodes. This fact can lead to changes in the quality of the links, affecting their reliability [2], [3]. Voluntary and involuntary movements cause shadowing, which affects the performance of the links; moreover, the shadow effect due to different body shapes has much influence in the path loss and in the distorted received signal [4].…”

Section: Introductionmentioning

confidence: 99%

A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels

Vallejo

Piorno

Rodrigo

2014

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

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Abstract-The human body has an important effect on the performance of on-body wireless communication systems. Given the dynamic and complex nature of the on-body channels, link quality estimation models are crucial in the design of mobility management protocols and power control protocols. In order to achieve a good estimation of link quality in WBSNs, we combine multiple body-related factors into a model that includes: the transmission power, the body position, the body shape and composition characteristics and the received signal strength indicator (RSSI) as an indicator of link quality. In this paper, we propose the Anfis Link Quality Estimator (A-LQE) that has been trained with RSSI values measured at different transmission power levels in a sample of 37 human subjects. Once the accuracy and reliability of our proposed model have been analysed, we apply the model to adapt the transmission power to the link characteristics for energy optimization. The obtained average energy savings reach the 26% in comparison with the maximum transmission power mode.

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Using body sensor networks for motion detection: a cluster‐based approach for green radio

Lan

Chou

Wang

et al. 2012

Trans. Emerging Tel. Tech.

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Body sensor networks (BSN) are an emerging application that places sensors on the human body. Given that a BSN is typically powered by a battery, one of the most critical challenges is how to prolong the lifetime of all sensor nodes. In addition, there is an increased interest in ‘green radio’ networks that aim to reduce the energy consumption for wireless communications to cut emissions of CO 2. Recently, using clusters to reduce the energy consumption of BSN has shown promising results. One of the important parameters in these cluster‐based algorithms is the selection of cluster heads. Most prior works selected cluster heads either probabilistically or on the basis of nodes’ residual energy. In this work, we first discuss the efficiency of cluster‐based approaches for saving energy. We then propose a novel cluster head selection algorithm to maximise the lifetime of a BSN for motion detection. Our results show that we can achieve above 90% accuracy for the motion detection, while keeping energy consumption as low as possible. Copyright © 2012 John Wiley & Sons, Ltd.

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Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring

Cited by 178 publications

References 26 publications

Ubiquitous emotion-aware computing

Ubiquitous emotion-aware computing

A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels

Using body sensor networks for motion detection: a cluster‐based approach for green radio

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