MicroLEAP: Energy-aware Wireless Sensor Platform for Biomedical Sensing Applications

Au, Lawrence; Wu, Winston; Batalin, Maxim; Mclntire, D.; Kaiser, William J.

doi:10.1109/biocas.2007.4463333

Cited by 45 publications

(36 citation statements)

References 8 publications

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“…In the system under study in this paper, we have used MicroLEAP as our main sensor node, which is responsible for sensing and transmitting the collected data from the sensors. Table I summarizes the power and energy consumption of the radio and the processor on MicroLEAP [Au et al 2007]. Therefore, one can easily conclude that in a network of N sensors, eliminating the sampling of a subset of the sensor data can drastically reduce the required energy consumption.…”

Section: Preliminariesmentioning

confidence: 99%

“…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%

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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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Section: Preliminariesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Behavior-oriented data resource management in medical sensing systems

Noshadi

Dabiri

Meguerdichian

et al. 2013

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

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“…This is because many BASNs, such as Hermes, are comprised of multi-sensory arrays wherein each node must power significantly more sensors than a WSN node, significantly increasing the energy demands of sampling. For example, the Hermes shoe, built on the MicroLEAP platform, consumes a total of 182.85 mW in active mode, with 72.74 mW consumed by the radio while transmitting at 115.2 kb/s [21] and 100.24 mW consumed in powering the 99 pressure sensors, where sampling power draw is derived from the maximum circuit voltage and force resistance curve for the underlying Flexiforce sensor [11]. Clearly, reducing the number of samples in an epoch from 99 to 1, yields upwards of 54% in power consumption reduction.…”

Section: Energy Consumption and Lifetimementioning

confidence: 99%

Fault-Tolerant and Low-Power Sampling Schedules for Localized BASNs

Goudar

Potkonjak

2013

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Abstract-Recent advances in the scope of wearable devices and networks make body area sensor networks (BASNs) an extremely attractive tool to the fields of mobile and tele-health, owing to the range of medical applications they can serve and the diagnostic richness of patient data they can offer. However, for BASNs to achieve true ubiquity, they must be scalable in their support of automated patient data collection, making usability and reliability key considerations. Its designers must wrestle with the tradeoff between usability, hindered by device intrusiveness into the behaviors it measures, and lifetime, enhanced by large power supplies and expensive, sturdy components. Furthermore, the validity and reliability of the collected data are paramount. In this paper, we consider these issues in the context of localized multi-sensory wearable networks and present a method to generate low-power sampling schedules that are resilient to sensor faults while achieving high diagnostic fidelity. We jointly formulate this as a power-constrained sampling problem wherein the number of sensors sampled per epoch are limited, and, a fault tolerant scheduling problem wherein the sampling scheme offers enough redundancy to endure up to a predefined number of sensor faults while maintaining diagnostic accuracy. This formulation is based on, 1) the localized scope of BASNs that engenders strong spatio-temporal interactions in the samples, and, 2) the periodic nature of human behaviors measured. We present our algorithm in the context of gait diagnostics derived from a foot plantar pressure measurement platform and illustrate its performance based on real datasets collected by it.Index Terms-Body area sensor networks, energy-efficient sampling, fault-tolerant sampling, power-constrained sampling.

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“…(ii) Designing sustainable computing platforms: Computing platforms have been developed to: (i) ensure sustainable and energy-efficient operations [31,32], and (ii) using eco-friendly materials [33][34][35].…”

Section: Equipment Recycling Perspectivementioning

confidence: 99%