Time series derived from simpler systems are single scale based and thus can be quantified by using traditional measures of entropy. However, times series derived from physical and biological systems are complex and show structures on multiple spatio-temporal scales. Traditional approaches of entropy based complexity measures fail to account for multiple scales inherent in these time series. Recently multi-scale entropy (MSE) method was introduced, which provide a way to measure complexity over a range of scales. MSE method uses sample entropy, a refinement of approximate entropy to quantify the complexity of time series. Nonstationarity, outliers and artifacts affect the sample entropy values because they change time series standard deviation and therefore, the value of similarity criterion. In this paper, we have used permutation entropy for quantifying the complexity, which is useful in the presence of dynamical and observational noise. We called this modified procedure multiscale permutation entropy (MPE). We observed that MPE is robust in presence of artifacts and robustly separates pathological and healthy groups

Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects’ wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state). Their ages were 27.2 ± 3.3 years and their body mass index (BMI) is 25.11 ± 2.6 Kg/m². Classification results demonstrated a high validity showing precision (a positive predictive value) and recall (sensitivity) of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients.

With the growing complexity of today's software applications injunction with the increasing competitive pressure has pushed the quality assurance of developed software towards new heights. Software testing is an inevitable part of the Software Development Lifecycle, and keeping in line with its criticality in the pre and post development process makes it something that should be catered with enhanced and efficient methodologies and techniques. This paper aims to discuss the existing as well as improved testing techniques for the better quality assurance purposes.