Tammara Massey

The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage

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.

Hardware Trojan horse detection using gate-level characterization

Hardware Trojan horses (HTHs) are the malicious altering of hardware specification or implementation in such a way that its functionality is altered under a set of conditions defined by the attacker. There are numerous HTHs sources including untrusted foundries, synthesis tools and libraries, testing and verification tools, and configuration scripts. HTH attacks can greatly comprise security and privacy of hardware users either directly or through interaction with pertinent systems and application software or with data. However, while there has been a huge research and development effort for detecting software Trojan horses, surprisingly, HTHs are rarely addressed. HTH detection is a particularly difficult task in modern and pending deep submicron technologies due to intrinsic manufacturing variability. Our goal is to provide an impetus for HTH research by creating a generic and easily applicable set of techniques and tools for HTH detection. We start by introducing a technique for recovery of characteristics of gates in terms of leakage current, switching power, and delay, which utilizes linear programming to solve a system of equations created using nondestructive measurements of power or delays. This technique is combined with constraint manipulation techniques to detect embedded HTHs. The effectiveness of the approach is demonstrated on a number of standard benchmarks.

Input vector control for post-silicon leakage current minimization in the presence of manufacturing variability

We present the first approach for post-silicon leakage power reduction through input vector control (IVC) that takes into account the impact of the manufacturing variability (MV). Because of the MV, the integrated circuits (ICs) implementing one design require different input vectors to achieve their lowest leakage states. We address two major challenges. The first is the extraction of the gate- level characteristics of an IC by measuring only the overall leakage power for different inputs. The second problem is the rapid generation of input vectors that result in a low leakage for a large number of unique ICs that implement a given design, but are different in the post-manufacturing phase. Experimental results on a large set of benchmark instances demonstrate the efficiency of the proposed methods. For example, the leakage power consumption could be reduced in average by more than 10.4%, when compared to the previously published IVC techniques that did not consider MV.

Participatory user centered design techniques for a large scale ad-hoc health information system

During mass casualty incidents, an enormous amount of data, including the vital signs of the patients, the location of the patients, and the location of the first responders must be gathered and communicated efficiently. The Advanced Health and Disaster Aid Network (AID-N) used participatory design methods to develop an electronic triage system that changed how emergency personnel interacted, collected, and processed data at mass casualty incidents. Through a collaboration between computer scientists, biomedical engineers, usability analysts, paramedics, and medical doctors, AID-N constructed scalable algorithms to monitor a large numbers of patients, an intuitive interface to support overwhelmed responders, and an ad-hoc mesh network that maintained connectivity to patients in ad-hoc, chaotic settings. This paper describes an iterative approach to user-centered design that allows for the collection of a massive amount of data and presents this data in a clear and understandable format to the user.

A Telehealth Architecture for Networked Embedded Systems: A Case Study in In Vivo Health Monitoring

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 patients 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/in2 with an accuracy of 0.02 lbf/in2. 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.

Opportunistic medical monitoring using bluetooth P2P networks

Remote medical monitoring with medical sensors and wireless communications has recently gained attention for the potential savings and improved quality in patient health-care. Currently, many mobile wireless medical infrastructures connect to the Internet in a point-to-point fashion, such as the cellular network. However, our research investigates new models for remote patient care that exploit P2P networking among patients and healthcare providers (nurses, doctors, emergency personnel). In this paper, we identify several medical applications based on P2P health networking focusing on two specific scenarios where nurses and patients are both equipped with Bluetooth devices. Nurses opportunistically collect, share and upload data in a P2P fashion. During an emergency, the nearest nurse is alerted via Bluetooth enhanced inquiry response mechanism. The simulation and test-bed experimentation demonstrate that Bluetooth P2P networking is both feasible and cost-effective in remote medical monitoring.

Experimental Analysis of a Mobile Health System for Mood Disorders

Depression is one of the leading causes of disability. Methods are needed to quantitatively classify emotions in order to better understand and treat mood disorders. This research proposes techniques to improve communication in body sensor network (BSN) that gathers data on the affective states of the patient. These BSNs can continuously monitor, discretely quantify, and classify a patients depressive states. In addition, data on the patients lifestyle can be correlated with his/her physiological conditions to identify how various stimuli trigger symptoms. This continuous stream of data is an improvement over a snapshot of localized symptoms that a doctor often collects during a medical examination. Our research first quantifies how the body interferes with communication in a BSN and detects a pattern between the line of sight of an embedded device and its reception rate. Then, a mathematical model of the data using linear programming techniques determines the optimal placement and number of sensors in a BSN to improve communication. Experimental results show that the optimal placement of embedded devices can reduce power cost up to 27% and reduce hardware costs up to 47%. This research brings researchers a step closer to continuous, real-time systemic monitoring that will allow one to analyze the dynamic human physiology and understand, diagnosis, and treat mood disorders.

General Methodology for Soft-Error-Aware Power Optimization Using Gate Sizing

Power consumption has emerged as the premier and most constraining aspect in modern microprocessor and application-specific designs. Gate sizing has been shown to be one of the most effective methods for power (and area) reduction in CMOS digital circuits. Recently, as the feature size of logic gates (and transistors) is becoming smaller and smaller, the effect of soft-error rates caused by single-event upsets (SEUs) is becoming exponentially greater. As a consequence of technology feature size reduction, the SEU rate for typical microprocessor logic at sea level will go from one in hundred years to one every minute. Unfortunately, the gate sizing requirements of power reduction and resiliency against SEU can be contradictory. 1) We consider the effects of gate sizing on SEU and incorporate the relationship between power reduction and SEU resiliency to develop a new method for power optimization under SEU constraints. 2) Although a nonlinear programming approach is a more obvious solution, we propose a convex programming formulation that can be solved efficiently. 3) Many of the optimal existing techniques for gate sizing deal with an exponential number of paths in the circuit. We prove that it is sufficient to consider a linear number of constraints. 4) We generalize our methodology to include nonlinear delay models and leakage power as well. As an important preprocessing step, we apply statistical modeling and validation techniques to quantify the impact of fault masking on the SEU rate. Furthermore, we adapt our method to incorporate process variation and evaluate our gate sizing technique under uncertainty. We evaluate the effectiveness of our methodology on ISCAS benchmarks and show that error rates can be reduced by a factor of 100%-200% while, on average, the power reduction is simultaneously decreased by less than 6%-10%, respectively, compared to the optimal power saving with no error rate constraints.

Rapid Dengue and Outbreak Detection with Mobile Systems and Social Networks

Dengue is a disease transmitted primarily through mosquito bites. Innovative solutions have been developed to combat outbreaks. However, in developing countries these dengue detection solutions are often not affordable and easily accessible. Additionally, these traditional approaches are slow to diagnose and treat dengue. We present a dengue detection solution that uses vision sensors in cellular phones, a lightweight object identification algorithm, and a web server that provides spatial information to healthcare providers. Our systems leverages a novel paper based technology developed by researchers at the Harvard University Department of Chemistry (Martinez et al. Angew Chem Int Ed 46:1318–1320, 2007). Our dengue detection algorithm rapidly diagnoses dengue, transmits the results to the Center for Disease Control (CDC) for further analysis, and presents healthcare providers with spatial information on outbreaks. This novel approach can improve the quality of life in developing countries by accurately and economically detecting dengue and providing data to the CDC for monitoring of dengue epidemics.

Low power light-weight embedded systems

Light-weight embedded systems are now gaining more popularity due to the recent technological advances in fabrication that have resulted in more powerful tiny processors with greater communication capabilities that pose various scientific challenges for researchers. Perhaps the most significant challenge is the energy consumption concern and reliability, mainly due to the small size of batteries. In this tutorial, we portray a brief description of low-power, light-weight embedded systems, depict several power profiling studies previously conducted, and present several research challenges that require low-power consumption in embedded systems. For each challenge, we highlight how low-power designs may enhance the overall performance of the system. Finally, we present a several techniques that minimize the power consumption in such systems