Sensor networks for medical care

Shnayder, Victor; Chen, Bor-Rong; Lorincz, Konrad; Jones, Thaddeus R. F. Fulford; Welsh, Matt

doi:10.1145/1098918.1098979

Cited by 559 publications

(346 citation statements)

References 38 publications

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“…., communicating the remote patient's status in real-time to caregivers, access to the expensive and large machines from anywhere in the hospital through wireless interface for rapid and flexible deployment, and etc.) [1][2][3]. It has been seen that current and potential applications of the wireless technologies in the medical field are ubiquitous.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Simulation Technique for Characterizing Wireless Channel in Medical Environments

Lee¹,

Bui²

2013

PIER

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Abstract-The purpose of this paper is to investigate the use of simulation technology for the analysis of wireless propagation channel in medical environments. In this paper, the channel modeling has been carried out by using an effective simulation platform, which combines full-wave Method of Moments and adaptive ray tracing technique. Base on this, the channel characteristics involving both large-scale and small-scale parameters of a wireless network deployed within a hospital environment can be estimated. Also, it is straightforward to predict the levels of electromagnetic field interference produced from the network infrastructure. The simulated results of four scenarios of medical environment, such as the patient room, the operating room, a particular level of the hospital, and the cardiac stress test room, with different wireless technologies used show the advantage and capability of the presented simulation approach.

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Section: Introductionmentioning

confidence: 99%

Hybrid Simulation Technique for Characterizing Wireless Channel in Medical Environments

Lee¹,

Bui²

2013

PIER

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“…Other implementations require structural changes to the home, such as instrumenting the floor [6,9] with force plates, which can incur high cost and effort. Many practical smart home applications such as in-home medical care for the elderly [2,17] and occupant-based energy monitoring [8] cannot use solutions that inconvenience the user, are intrusive, or require an expensive building retrofit. Our recent discussions with a commercial peace of mind elderly monitoring enterprise [4] reveal several interesting user requirements for accurate, long term elderly resident identification and tracking in homes: (1) residents will not wear tags or manually identify themselves at every room for long periods of time, (2) residents will not allow perceived invasive devices such as cameras or microphones in the home, and (3) residents want the sensors to be fairly invisible, similar to existing motion sensor installations, and do not want an expensive building retrofit.…”

Section: Introductionmentioning

confidence: 99%

Using Height Sensors for Biometric Identification in Multi-resident Homes

Srinivasan

Stankovic

Whitehouse

2010

Lecture Notes in Computer Science

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Abstract. In this study, we evaluate the use of height for biometric identification of residents, by mounting ultrasonic distance sensors above the doorways in a home. Height sensors are cheap, are convenient for the residents, are simple to install in an existing home, and are perceived to be less invasive than cameras or microphones. Height is typically only a weak biometric, but we show that it is well suited for identifying among a few residents in the home, and can potentially be improved by using the history of height measurements at multiple doorways in a tracking approach. We evaluate this approach using 20 people in a controlled laboratory environment and by installing in 3 natural, home environments. We combine these results with public anthropometric data sets that contain the heights of residents in 2077 elderly multi-resident homes to conclude that height sensors could potentially achieve at least 95% identification accuracy in 95% of elderly homes in the US.

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“…Typical prototype devices in use today, such as MicaZ motes [1] used in Harvard's CodeBlue [2] project, operate on a pair of AA batteries that provide a few tens of kilo-Joules of energy. In contrast, emerging truly-wearable health monitoring devices such as the "digital plasters" being developed at Toumaz Technologies have orders of magnitude lower battery capacity (the Toumaz Sensium TM chip shown in figure 1 operates on a flexible paper-thin printed battery [3] with a capacity of approximately 70 Joules).…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Transmission Power Control in Biomedical Wireless Sensor Networks

Dhamdhere

Sivaraman

Mathur

et al. 2008

2008 IEEE Asia-Pacific Services Computing Conference

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Abstract-Wireless sensor networks are increasingly being used for continuous monitoring of patients with chronic health conditions such as diabetes and heart problems. As biomedical sensor nodes become more wearable, their battery sizes diminish, necessitating very careful energy management. This paper proposes feedback-based closed-loop algorithms for dynamically adjusting radio transmit power in body-worn devices, and evaluates their performance in terms of energy savings and reliability as the data periodicity and feedback time-scales vary. Using experimental trace data from body worn devices, we first show that the performance of dynamic power control is adversely affected at long data periods. Next for a given data period we show that modifying the transmit power at too long timescales (around a minute) reduces the efficacy of dynamic power control, while too short a time-scale (few seconds or less) incurs a high feedback signaling overhead. We therefore advocate an intermediate range of time-scales (when permitted by the data periodicity), typically in the few tens of seconds, at which the control algorithms should adapt transmit power in order to achieve maximal energy savings in body-worn sensor devices used for medical monitoring.

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Sensor networks for medical care

Cited by 559 publications

References 38 publications

Hybrid Simulation Technique for Characterizing Wireless Channel in Medical Environments

Hybrid Simulation Technique for Characterizing Wireless Channel in Medical Environments

Using Height Sensors for Biometric Identification in Multi-resident Homes

Algorithms for Transmission Power Control in Biomedical Wireless Sensor Networks

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