Thermoelectric mems generators as a power supply for a body area network

Leonov, Vladimir; Fiorini, Paolo; Sedky, Sherif; Torfs, Tom; Hoof, Chris Van

doi:10.1109/sensor.2005.1496414

Cited by 84 publications

(44 citation statements)

References 1 publication

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“…As shown in Figure 5b, the optimum FF of a given material A-D is always below 10%. On the other hand, in order to utilize thermoelectric elements in power generation applications, a sufficiently high output voltage must be ensured [40]. Since 1 couple of TE legs cannot generate more than 1 mV for 1 K of temperature difference, a thermoelectric device should have many TE legs, which increases the fill factor of the device.…”

Section: Fill Factor Variationmentioning

confidence: 99%

Material Optimization for a High Power Thermoelectric Generator in Wearable Applications

et al. 2017

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Thermoelectric power generation using human body heat can be applied to wearable sensors, and various applications are possible. Because the thermoelectric generator (TEG) is highly dependent on the thermoelectric material, research on improving the performance of the thermoelectric material has been conducted. Thus far, in developing thermoelectric materials, the researchers have focused on improving the figure of merit, ZT. For a TEG placed on the human body, however, the power density does not always increase as the material ZT increases. In this study, the material properties and ZT of P-type BiSbTe 3 were simulated for carrier concentration ranging from 3 × 10 17 to 3 × 10 20 cm −3 , and the power density of a TEG fabricated from the material dataset was calculated using a thermoelectric resistance model for human body application. The results revealed that the maximum ZT and the maximum power density were formed at different carrier concentrations. The material with maximum ZT showed 28.8% lower power density compared to the maximum obtainable power density. Further analysis confirmed that the mismatch in the optimum carrier concentration for the maximum ZT and maximum power density can be minimized when a material with lower thermal conductivity is used in a TEG. This study shows that the ZT enhancement of materials is not the highest priority in the production of a TEG for human body application, and material engineering to lower the thermal conductivity is required to reduce the optimum point mismatch problem.

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Section: Fill Factor Variationmentioning

confidence: 99%

Material Optimization for a High Power Thermoelectric Generator in Wearable Applications

et al. 2017

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“…It is shown in [50] that it is possible to use human body as a heat source. Because of different kinds of tissues and fluids inside body, human body has non-uniform temperature distribution.…”

Section: Thermoelectric Generationmentioning

confidence: 99%

“…Because of different kinds of tissues and fluids inside body, human body has non-uniform temperature distribution. A micro-generator is fabricated using an array of polysilicon-germanium (poly-SiGe) thermocouples by Leonov et al [50] which is mounted to wrist to allow heat absorption directly from the radial artery. Its package size is 3×3×1cm 3 and it is able to generate 4.5μW/cm 2 of power on the radial artery.…”

Section: Thermoelectric Generationmentioning

confidence: 99%

MRI-based communication for untethered intelligent medical microrobots

Sharafi

Olamaei

2015

J Micro-Bio Robot

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“…Thus, it has been targeted by a range of researchers from communication and signal processing to hardware design and software engineering in body area networks. The energy optimization has been addressed on the hardware level, where either hardware responsible for data acquisition and transmission is designed to be energy efficient [Au et al 2007] or energy scavenging techniques are proposed to make the wearable system power autonomous [Leonov et al 2005]. Power efficient sensing algorithms and strategies also have been proposed to address the power issue by using smart sensor placement, sensing, and transmission [Ghasemzadeh et al 2008] [Liu et al 2007] [Yan et al 2007] [Xiao et al 2009].…”

Section: Related Workmentioning

confidence: 99%

Behavior-oriented data resource management in medical sensing systems

Noshadi

Dabiri

Meguerdichian

et al. 2013

ACM Trans. Sen. Netw.

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Wearable sensing systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring leads these systems to be power hungry and expensive, with short operating lifetimes. We introduce a novel methodology that takes advantage of contextual and semantic properties in human behavior to enable efficient design and optimization of such systems from the data and information point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system contextual behavior. Our goal is to select a small subset of sensors that accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and a simultaneous minimization composed of subset sensor selection and opportunistic sampling. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 72% to 97% for error rates of 5% to 17.5%.

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Thermoelectric mems generators as a power supply for a body area network

Cited by 84 publications

References 1 publication

Material Optimization for a High Power Thermoelectric Generator in Wearable Applications

Material Optimization for a High Power Thermoelectric Generator in Wearable Applications

MRI-based communication for untethered intelligent medical microrobots

Behavior-oriented data resource management in medical sensing systems

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