Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients.

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 non-destructive 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.

Reconfiguration delay is one of the major barriers in the way of dynamically adapting a system to its application requirements. The run-time reconfiguration delay is quite comparable to the application latency for many classes of applications and might even dominate the application run-time. In this paper, we present an efficient optimal algorithm for minimizing the run-time reconfiguration (context switching) delay of executing an application on a dynamically adaptable system. The system is composed of a number of cameras with embedded reconfigurable resources collaborating in order to track an object. The operations required to execute in order to track the object are revealed to the system at run-time and can change according to a number of parameters, such as the target shape and proximity. Similarly, we can assume that the applications comprising tasks are already scheduled and each of them has to be realized on the reconfigurable fabric in order to be executed.The modeling and the algorithm are both applicable to partially reconfigurable platforms as well as multi-FPGA systems. The algorithm can be directly applied to minimize the application runtime for the typical classes of applications, where the actual execution delay of the basic operations is negligible compared to the reconfiguration delay. We prove the optimality and the efficiency of our algorithm. We report the experimental results, which demonstrate a 2.5-40% improvement on the total run-time reconfiguration delay as compared to other heuristics.

Fall-induced injury has become a leading cause of death for the elderly. Many elderly people rely on canes as an assistive device to overcome problems such as balance disorder and leg weakness, which are believed to have led to many incidents of falling. In this paper, we present the design and the implementation of SmartFall, an automatic fall detection system for the SmartCane system we have developed previously. SmartFall employs subsequence matching, which differs fundamentally from most existing fall detection systems based on multi-stage thresholding. The SmartFall system achieves a near perfect fall detection rate for the four types of fall conducted in the experiments. After augmenting the algorithm with an assessment on the peak impact force, we have successfully reduced the false-positive rate of the system to close to zero for all six non-falling activities performed in the experiment.

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Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches to detect letters written in the air by a user. This method is less computationally intensive and less expensive when compared to computer vision approaches. It is also not affected by lighting factors, which limit computer vision solutions. The AirDraw system prototype developed to test this approach is presented. Additionally, experimental results close to 71% accuracy are presented.

Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial.