Smartwatch: performance evaluation for long-term heart rate monitoring

Recent advancement in wearable technologies, particularly smart watches embedded with powerful processors, memory subsystems with various built-in sensors such as ac-celerometer, gyroscope and optical sensor in one single package has opened a whole new application space. One of the main applications of interest is the monitoring of movement patterns, heart rate, ECG and PPG particularly for longer duration's in natural environments. In this study, we conducted a performance evaluation on the optical heart rate sensor of the smartwatch with respect to the commonly used ECG and PPG devices. Results have shown that the heart rate acquired from the smartwatch is reasonably accurate with a high degree of correlation. Further, we conducted a preliminary exerise to evaluate sleep quality using the heart rate readings and accelerometer readings captured from the smartwatch and compared with a commercially available and clinically used non-contact sleep sensor, RESMED S+.

Improving tenants' trust in SaaS applications using dynamic security monitors

It is almost impossible to prove that a given software system achieves an absolute security level. This becomes more complicated when addressing multi-tenant cloud-based SaaS applications. Developing practical security properties and metrics to monitor, verify, and assess the behavior of such software systems is a feasible alternative to such problem. However, existing efforts focus either on verifying security properties or security metrics but not both. Moreover, they are either hard to adopt, in terms of usability, or require design-time preparation to support monitoring of such security metrics and properties which is not feasible for SaaS applications. In this paper, we introduce, to the best of our knowledge, the first unified monitoring platform that enables SaaS application tenants to specify, at run-time, security metrics and properties without design-time preparation and hence increases tenants’ trust of their cloud-assets security. The platform automatically converts security metrics and properties specifications into security probes and integrates them with the target SaaS application at run-time. Probes-generated measurements are fed into an analysis component that verifies the specified properties and calculates security metrics’ values using aggregation functions. This is then reported to SaaS tenants and cloud platform security engineers. We evaluated our platform expressiveness and usability, soundness, and performance overhead.

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion. © 2010 Elsevier Ltd. All rights reserved.