Managing care through the air [remote health monitoring

Ross, Penny

doi:10.1109/mspec.2004.1363637

Cited by 105 publications

(8 citation statements)

References 1 publication

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“…Various applications using big data generated by densely distributed WSNs have also emerged in literature. In addition, in [14] and [15], big data in terms of the healthcare information (e.g., blood pressure and heart rate) sensed by numerous sensors are used to realize remote medical care services. Furthermore, patients' location information are used to arrange prompt dispatch of ambulances.…”

Section: Related Work and Our Motivationmentioning

confidence: 99%

Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

Takaishi

Nishiyama

Kato

et al. 2014

IEEE Trans. Emerg. Topics Comput.

124

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Recently, the big data emerged as a hot topic because of the tremendous growth of the information and communication technology. One of the highly anticipated key contributors of the big data in the future networks is the distributed wireless sensor networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Energy-efficient big data gathering in the densely distributed sensor networks is, therefore, a challenging research area. One of the most effective solutions to address this challenge is to utilize the sink node's mobility to facilitate the data gathering. While this technique can reduce energy consumption of the sensor nodes, the use of mobile sink presents additional challenges such as determining the sink node's trajectory and cluster formation prior to data collection. In this paper, we propose a new mobile sink routing and data gathering method through network clustering based on modified expectation-maximization technique. In addition, we derive an optimal number of clusters to minimize the energy consumption. The effectiveness of our proposal is verified through numerical results.INDEX TERMS Big data, wireless sensor networks (WSNs), clustering, optimization, data gathering, and energy efficiency.

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Section: Related Work and Our Motivationmentioning

confidence: 99%

Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

Takaishi

Nishiyama

Kato

et al. 2014

IEEE Trans. Emerg. Topics Comput.

124

View full text Add to dashboard Cite

show abstract

“…2, (2) where t x is the combined rise and fall time of the switches in the Buck converter. The losses introduced by the multiplier are determined by the inductor current ripple and the supply voltage of the transconductor (Gm), as shown in Figure 9.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…While cell phones have swallowed digital cameras, game consoles, and media players without gaining any weight, similarly thin, light electronic organizers now boast of their talents for wireless web browsing and global positioning. Further integration could feature all of this functionality in a single chip and new intelligent sensors embedded in everything from clothing to coffee mugs [1], [2]. However, these dreams imply conflicting requirements for power management circuits, which need large passive elements to reliably support all of these new features.…”

Section: Introductionmentioning

confidence: 99%

A novel predictive inductor multiplier for integrated circuit DC-DC converters in portable applications

Milner

Rincón-Mora

2005

Proceedings of the 2005 International Symposium on Low Power Electronics and Design - ISLPED '05

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While the large passive elements of power converters are in the way of converging walls of shrinking cell phones and cameras, the new capabilities these devices flaunt are creating additional burdens and making it difficult to meet specifications without even bigger elements. Active circuits that enhance the effects of passive elements will allow power converters to handle larger loads and get smaller at the same time. This paper presents a predictive inductor multiplier circuit that amplifies the effective inductance in a Buck converter. The output ripple of the simulated converter is so small that the converter appears to have an inductance thirty-eight times the value actually used. Compensating for small inductors introduces new power losses, but it is discovered that linear regulators and faster switching converters can be even less efficient.

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“…Ohta et al [2002] propose a system in which infrared sensors are deployed in every room, from which the health condition of the household occupants is estimated by comparing the duration of stays in specific rooms. Ross [2004] discuss the use of Motes, a sensor developed by Intel, designed to be attached to everyday household items, such as clothes, cups or medicine containers. Such sensors collected data from which ADLs can be inferred.…”

Section: Health Monitoringmentioning

confidence: 99%

Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes

Alcala

Parson

Rogers

2015

Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments

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Monitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country's ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household's occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark.

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Managing care through the air [remote health monitoring

Cited by 105 publications

References 1 publication

Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

A novel predictive inductor multiplier for integrated circuit DC-DC converters in portable applications

Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes

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