This paper presents system to translate gestures of Urdu Sign language using an instrumented wearable glove. This system contributes first ever attempt in terms of fabrication of Pakistani Sign Language translating glove which is portable as well as cost effective. As the sensor values from the glove vary from person to person, this system was made to use pattern recognition approach. In order to accomplish the task, Principal Component Analysis (PCA) was employed for feature extraction and Euclidean distance as classification technique. Up till now the system has ten static gestures in its library and it perfectly judges nine gestures out of targeted ten gestures which are commonly used in Urdu Sign language.

BackgroundNon-communicable diseases (NCD) are the leading causes of death globally. In Pakistan, they are among the top ten causes of mortality, especially in the productive age group (30–69 years). Evidence suggests that health perceptions and beliefs strongly influence the health behavior of an individual. We performed focus group interviews to delineate the same so as to design the user interface of a non-invasive stroke risk monitoring device.MethodsIt was a qualitative study, designed to explore how health perceptions and beliefs influence behavior for NCD prevention. Four focus group discussions (FGD) were conducted with 30 stable participants who had diabetes mellitus, ischemic heart disease, blood pressure, and stroke. The data was collected using a semi-structured interview guide designed to explore participants’ perceptions of their illnesses, self-management behaviors and factors affecting them. The interviews were transcribed and content analysis was done using steps of content analysis by Morse and Niehaus [10].ResultsMedication adherence, self-monitoring of blood sugars and blood pressures, and medical help seeking were the commonly performed self-management behaviors by the participants. Personal experience of illness, familial inheritance of disease, education and fear of premature death when life responsibilities were unfulfilled, emerged as strong facilitators of self-management behaviors. A sense of personal invincibility, Fatalism or inevitability, lack of personal threat realization, limited knowledge, inadequate health education, health care and financial constraints appeared as key barriers to the self-management of chronic disease in participants.ConclusionsBehavioural interventional messaging will have to engender a sense of personal vulnerability and yet empower self-efficacy solutions at the individual level to deal with both invincibility and inevitability barriers to adoption of healthy behavior.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.