Abstract-Advances in semiconductor technology have resulted in the creation of miniature medical embedded systems that can wirelessly monitor the vital signs of patients. These lightweight medical systems can aid providers in large disasters who become overwhelmed with the large number of patients, limited resources, and insufficient information. In a mass casualty incident, small embedded medical systems facilitate patient care, resource allocation, and real-time communication in the Advanced Health and Disaster Aid Network (AID-N). We present the design of electronic triage tags on lightweight, embedded systems with limited memory and computational power. These electronic triage tags use noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure cuff) to continuously monitor the vital signs of a patient and deliver pertinent information to first responders. This electronic triage system facilitates the seamless collection and dissemination of data from the incident site to key members of the distributed emergency response community. The real-time collection of data through a mesh network in a mass casualty drill was shown to approximately triple the number of times patients that were triaged compared with the traditional paper triage system.

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A fully implantable wireless pressure sensor system was developed to monitor bladder pressures in vivo. The system comprises a small commercial pressure die connected via catheter to amplifying electronics, a microcontroller, wireless transmitter, battery, and a personal digital assistant (PDA) or computer to receive the wireless data. The sensor is fully implantable and transmits pressure data once every second with a pressure detection range of 1.5 psi gauge and a resolution of 0.02 psi. In vitro calibration measurements of the device showed a high degree of linearity and excellent temporal response. The implanted device performed continuously in vivo in several porcine studies lasting over 3 days. This system can be adapted for other pressure readings, as well as other vital sign measurements; it represents the first step in developing a ubiquitous sensing platform for telemedicine and remote patient monitoring.

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

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%.

In recent years, exciting technological advances have been made in development of flexible electronics. These technologies offer the opportunity to weave computation, communication and storage into the fabric of the every clothing that we wear, therefore, creating intelligent fabric. This paper presents a medical vest which has sensors for physiological readings and software-controlled, electrically-actuated trans-dermal drug delivery elements. Furthermore, computational elements are embedded in the vest for collecting data from sensors, processing them and driving actuation elements. Since this vest will be used for medical, life-critical applications, the single most critical requirement of such a vest is an extremely high level of robustness and fault tolerance. Meantime, the key technological constraint for these mobile systems is their power consumption. Our target application for our medical vest is the detection of possibly fatal heart problems, specifically unstable angina pectoris or ischemia. We illustrate the design stages of our medical vest as well as the technical details of both software and network reconfiguration schemes (to enhance the robustness and the performance of our system). We also discuss the details of ischemia detection algorithm employed in our vest. Moreover, we evaluate the robustness of our system with existence of various faults. Finally we measure the performance of our algorithm as well the power consumption of several configurations of our vest.

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