Hyduke Noshadi
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Behavior-Oriented Data Resource Management in Medical Sensing Systems
Foad Dabiri
Saro Meguerdichian
Miodrag Potkonjak
Majid Sarrafzadeh
ACM Transactions on Sensor Networks (TOSN), 9 (2013), 12:1-12:26
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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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HERMES: Mobile system for instability analysis and balance assessment
Foad Dabiri
Shaun Ahmadian
Navid Amini
Majid Sarrafzadeh
ACM Transactions on Embedded Computing Systems (TECS), 12 (2013), 57:1-57:24
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We introduce Hermes, a lightweight smart shoe and its supporting infrastructure aimed at extending gait and instability analysis and human instability/balance monitoring outside of a laboratory environment. We aimed to create a scientific tool capable of high-level measures, by combining embedded sensing, signal processing and modeling techniques. Hermes monitors walking behavior and uses an instability assessment model to generate quantitative value with episodes of activity identified by physician, researchers or investigators as important. The underlying instability assessment model incorporates variability and correlation of features extracted during ambulation that have been identified by geriatric motion study experts as precursor to instability, balance abnormality and possible fall risk. Hermes provides a mobile, affordable and long-term instability analysis and detection system that is customizable to individual users, and is context-aware, with the capability of being guided by experts. Our experiments demonstrate the feasibility of our model and the complimentary role our system can play by providing long-term monitoring of patients outside a hospital or clinical setting at a reduced cost, with greater user convenience, compliance and inference capabilities that meet the physician's or investigator's needs.
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Behavioural reconfigurable and adaptive data reduction in body sensor networks
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Foad Dabiri
Majid Sarrafzadeh
International Journal of Autonomous and Adaptive Communications Systems, 6 (2013), pp. 207-224
Semantics-driven sensor configuration for energy reduction in medical sensor networks
Saro Meguerdichian
Miodrag Potkonjak
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design, ACM, pp. 303-308
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Traditional optimization methods for large multisensory networks often use sensor array reduction and sampling techniques that attempt to reduce energy while retaining full predictability of the raw sensed data. For systems such as medical sensor networks, raw data prediction is unnecessary, rather, only relevant semantics derived from the raw data are essential. We present a new method for sensor fusion, array reduction, and subsampling that reduces both energy and cost through semantics-driven system configuration. Using our method, we reduce the energy requirements of a medical shoe by a factor of 17.9 over the original system configuration while maintaining semantic relevance.
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Energy and Cost Reduction in Localized Multisensory Systems through Application-Driven Compression
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Saro Meguerdichian
Miodrag Potkonjak
Data Compression Conference (DCC), IEEE (2012), pp. 411
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We present a new method for spatiotemporal assignment and scheduling of energy harvesters on a medical shoe tasked with measuring gait diagnostics. While prior work exists on the application of dielectric elastomers (DEs) for energy scavenging on shoes, current literature does not address the issues of placement and timing of these harvesters, nor does it address integration into existing sensing systems. We solve these issues and present a self-sustaining medical shoe that harvests energy from human ambulation while simultaneously measuring gait characteristics most relevant to medical diagnosis.
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Joint consideration of energy-efficiency and coverage-preservation in microsensor networks
Navid Amini
Alireza Vahdatpour
Foad Dabiri
Majid Sarrafzadeh
Wireless Communications & Mobile Computing, 11 (2011), pp. 707-722
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This paper presents an energy-efficient and coverage-preserving communication protocol which distributes a uniform energy load to the sensors in a wireless microsensor network. This protocol, called Distance-based Segmentation (DBS), is a cluster-based protocol that divides the entire network into equal-area segments and applies different clustering policies to each segment to (1) reduce total energy dissipation and (2) balance the energy load among the sensors. Therefore, it prolongs the lifetime of the network and improves the sensing coverage. Moreover, the proposed routing protocol does not need any centralized support from a certain node which is at odds with aiming to establish a scalable communication protocol. Results from extensive simulations on two different network configurations show that by lowering the number of wasteful transmissions in the network, the DBS can achieve as much as a 20% reduction in total dissipated energy as compared with current cluster-based protocols. In addition, this protocol is able to distribute energy load more evenly among the sensors in the network. Hence, it yields up to a 66% increase in the useful network lifetime. According to the simulation results, the sensing coverage degradation of the DBS is considerably slower than that of the other cluster-based protocols.
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Semantic Multimodal Compression for Wearable sensing Systems
Saro Meguerdichian
Foad Dabiri
Miodrag Potkonjak
In Proceedings of the 9th Annual IEEE Conference on Sensors, IEEE (2010), pp. 1149-1453
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Wearable sensing systems (WSS's) are emerging as an important class of distributed embedded systems in application domains ranging from medical to military. Such systems can be expensive and power hungry due to their multi sensor implementations that require constant use, yet by nature they demand low-cost and low-power implementations. Semantic multimodal compression (SMC) mitigates these metrics in terms of data size by leveraging the natural tendency of signals in many types of embedded sensing systems to be composed of phases. In our driving example of a medical shoe with an insole lined with pressure sensors, we find that the natural airborne, landing, and take-off segments have sharply different and repetitive properties. SMC models and compresses each segment independently, selecting the best compression scheme for each segment and thus reducing total transmission energy.
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Energy Optimization in Wireless Medical Systems Using Physiological Behavior
Foad Dabiri
Saro Meguerdichian
Miodrag Potkonjak
Majid Sarrafzadeh
In Proceedings of the ACM, BMES conference of Wireless Health, ACM (2010), pp. 128-136
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Wearable sensing systems are becoming widely used for a variety of applications, including sports, entertainment, and military. These systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring lead these systems to be power hungry and expensive, with short operating lifetimes. In this paper, we introduce a novel methodology that takes advantage of the influence of human behavior on signal properties and reduces those three metrics from the data size 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 behavior. Our goal is to select a small subset of sensors to 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 subset sensor selection. 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 56% to 96% for error rates of 5% to 17.5%.
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Remote Medical Monitoring Through Vehicular Ad Hoc Network
Eugenio Giordano
Hagop Hagopian
Giovanni Pau
Mario Gerla
Majid Sarrafzadeh
IEEE 68th Vehicular Technology Conference (VTC), 2008., IEEE, pp. 1-5
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Several diseases and medical conditions require constant monitoring of physiological signals and vital signs on daily bases, such as diabetics, hypertension and etc. In order to make these patients capable of living their daily life it is necessary to provide a platform and infrastructure that allows the constant collection of physiological data even when the patient is not inside of the coverage area. The data must be rapidly "transported" to care givers or to the designated medical enterprise. The problem is particularly severe in case of emergencies (e.g. natural disasters or hostile attacks) when the communications infrastructure (e.g. cellular telephony, WiFi public access, etc) has failed or is totally congested. In this paper we present an evaluation of of the vehicular ad-hoc networks (VANET) as an alternate method of collecting patient pre-recorded physiological data and at the same time reconfiguring patient medical wearable body vests to select the data specifically requested by the physicians. Another important use of vehicular collection of medical data from body vests is prompted by the need to correlate pedestrian reaction to vehicular traffic hazards such as chemical and noise pollution and traffic congestion. The vehicles collect noise, chemical and traffic samples and can directly correlate with the "stress level" of volunteers.
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