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.

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%.

Localized multisensory systems for medical diagnostics are becoming increasingly important due to the prevalence of lightweight sensors and new collection, storage, and communication platforms such as mobile phones and tablets. These systems are often comprised of multisensory arrays that can be inefficient in terms of energy and cost both in sensing and transmission. We make the following two key observations: (i) the raw sensed data itself is unimportant, only those metrics relevant to diagnosis are needed, and (ii) it is often the case that the information relevant to medical diagnosis can be easily derived from the raw sensed data. Therefore, we drive our energy optimization procedure by selecting a subset of sensors that can predict these metrics well, eliminating all others and ultimately reducing the number of required sensors. We also develop a novel procedure for combining adjacent sensors to further reduce cost and sensing energy while increasing prediction strength. Finally, we present an algorithm for sub sampling the selected sensors that leverages the observations that: (i) most sensors need only be sampled after a physiological event is triggered, and (ii) such events tend to be predictable from semantic information and therefore a high sampling rate is unnecessary.

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.

Abstract 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. Copyright © 2009 John Wiley & Sons, Ltd.

The improvement in processor performance through continuous breakthroughs in transistor technology has resulted in the proliferation of lightweight embedded systems. Advances in wireless technology and embedded systems have enabled remote healthcare and telemedicine. While medical examinations could previously extract only localized symptoms through snapshots, now continuous monitoring can discretely analyze how a patient's lifestyle affects his/her physiological conditions and if additional symptoms occur under various stimuli. We demonstrate how medical applications in particular benefit from a hierarchical networking scheme that will improve the quantity and quality of ubiquitous data collection. Our Telehealth networking infrastructure provides flexibility in terms of functionality and the type of applications that it supports. We specifically present a case study that demonstrates the effectiveness of our networked embedded infrastructure in an in vivo pressure application. Experimental results of the in vivo system demonstrate how it can wirelessly transmit pressure readings measuring from 0 to 1.5 lbf/in (2) with an accuracy of 0.02 lbf/in (2). The challenges in biocompatible packaging, transducer drift, power management, and in vivo signal transmission are also discussed. This research brings researchers a step closer to continuous, real-time systemic monitoring that will allow one to analyze the dynamic human physiology.

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.

Tiny embedded systems have not been an ideal outfit for high performance computing due to their constrained resources-limitations in processing power, battery life, communication bandwidth, and memory constrain the applicability of existing complex medical analysis algorithms such as the electrocardiogram (ECG) analysis. Among various limitations, battery lifetime has been a major key technological constraint. In this paper, we address the issue of partitioning such a complex algorithm while the energy consumption due to wireless transmission is minimized. ECG analysis algorithms normally consist of preprocessing, pattern recognition, and classification. Considering the orientation of the ECG leads, we devise a technique to perform preprocessing and pattern recognition locally in small embedded systems attached to the leads. The features detected in the pattern recognition phase are considered for the classification. Ideally, if the features detected for each heartbeat reside in a single processing node, the transmission will be unnecessary. Otherwise, to perform classification, the features must be gathered on a local node and, thus, the communication is inevitable. We perform such a feature grouping by modeling the problem as a hypergraph and applying partitioning schemes which yield a significant power saving in wireless communications. Furthermore, we utilize dynamic reconfiguration by software module migration. This technique, with respect to partitioning, enhances the overall power saving in such systems. Moreover, it adaptively alters the system configuration in various environments and on different patients. We evaluate the effectiveness of our proposed techniques on MIT/BIH benchmarks and, on average, achieve 70 percent energy saving

We propose a platform for health monitoring using wireless sensor networks. Our platform is a new architecture called CustoMed that will reduce the customization and reconfiguration time for medical systems that use reconfigurable embedded systems. This architecture is a network enabled system that supports various wearable sensors and contains on-board general computing capabilities for executing individually tailored event detection, alerts, and network communication with various medical informatics services. The customization of such system with a large number of "med nodes" is extremely fast even by non-engineering staff. In this paper, we present the architecture of such device along with experimental analysis that evaluates the performance of such system.