Obstructive sleep apnea (OSA) is the most common form of different types of sleep-related breathing disorders. It is characterized by repetitive cessations of respiratory flow during sleep, which occurs due to a collapse of the upper respiratory airway. OSA is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying the presence of sleep apnea based on the acoustic signal of respiration. The characterization of breathing sound was carried by Voice Activity Detection (VAD) algorithm, which is used to measure the energy of the acoustic respiratory signal during breath and breath hold. The performance of our classification algorithm is tested on real respiratory signals and the experimental results show that the VAD is useful as a predictive tool for the segmentation of breath into sound and silence segments. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening

Monitoring breath and identifying breathing movements have settled importance in many biomedical research areas, especially in the treatment of those with breathing disorders, e.g., lung cancer patients. Moreover, virtual reality (VR) revolution and their implementations on ubiquitous hand-held devices have a lot of implications, which could be used as a simulation technology for healing purposes. In this paper, a novel method is proposed to detect and classify breathing movements. The overall VR framework is intended to encourage the subjects regulate their breath by classifying the breathing movements in real time. This paper focuses on a portion of the overall VR framework that deals with classifying the acoustic signal of respiration movements. We employ Mel-frequency cepstral coefficients (MFCCs) along with speech segmentation techniques using voice activity detection and linear thresholding to the acoustic signal of breath captured using a microphone to depict the differences between inhale and exhale in frequency domain. For every subject, 13 MFCCs of all voiced segments are computed and plotted. The inhale and exhale phases are differentiated using the sixth MFCC order, which carries important classification information. Experimental results on a number of individuals verify our proposed classification methodology.

This paper presents a conceptual framework of a virtual reality therapy to assist individuals, especially lung cancer patients or those with breathing disorders to regulate their breath through real-time analysis of respiration movements using a smartphone. Virtual reality technology is an attractive means for medical simulations and treatment, particularly for patients with cancer. The theories, methodologies and approaches, and real-world dynamic contents for all the components of this virtual reality therapy (VRT) via a conceptual framework using the smartphone will be discussed. The architecture and technical aspects of the offshore platform of the virtual environment will also be presented.

Purpose: Generally, the use of cloud-based services has proliferated all around the world. This tendency increased dramatically during the coronavirus disease 2019 (COVID-19) pandemic when many people were obliged to conduct business remotely. This research study aims to investigate the intentions to adopt (IA) cloud-based enterprise resource planning (ERP) systems in Jordan, as this sort of research is scarce in developing countries. Specifically, this research study combined the model of Diffusion of Innovation (DOI) and the Technology-Organization-Environment (TOE) framework, in addition to looking at COVID-19 as a moderating variable.Design/methodology/approach: Data were collected from 60 participants working in Jordanian companies that currently use, or have the intention to use, cloud-based ERP systems. An online questionnaire was developed and used to collect responses. The study hypotheses were examined statistically using simple regression analysis and hierarchical multiple regression.Findings: The study results clearly indicate that complexity, observability, employee information technology (IT) skills, organizational culture (OC) and top management support significantly affect the intention to adopt cloud-based ERP systems. In contrast, relative advantage (RA), compatibility, trialability, regulatory environment and competitive pressure had no significant effect on adoption intention. Interestingly, the advent of COVID-19 increased the intention to adopt cloud-based ERP systems.Research limitations/implications: The study's findings will provide theoretical and practical suggestions for the effective adoption of cloud ERP in Jordan, as well as aiding other developing nations in comparable situations in implementing cloud-based ERP systems.Originality/value: The study enriches the knowledge and further examines the underlying reasons for adopting cloud-based ERP systems in a developing country.

Breathing disorders are generally associated with the lung cancer disease. Through daily treatment, lung cancer patients often use a traditional spirometer to measure their lung capacity. However, the use of a spirometer device for accurate measurement requires some sort of training and adjustment, which may be inconvenient for certain groups of patients, especially the elderly. In addition, the spirometer readings can become unreliable if the measurements are not taken as instructed. On the other hand, a microphone, say the microphone on a hand-held device such as a smart-phone, can easily capture the acoustic signal of breath without certain instructions. This signal can then be processed to estimate the lung capacity. In this paper, we propose a methodology through the simply recorded acoustic signal of breath, splitting the breathing cycle to inhale, pause, exhale and pause phases to measure the depth of the breath along with the time duration and signal energy of the breathing phases. We show how these computed parameters are used to estimate the lung size with a high degree of accuracy. This work is part of a virtual reality platform embedded within a smart-phone to assist lung cancer patients regulate their breath. Furthermore, the lung capacity estimation methodology proposed in this paper can also be used to aid patients with other breathing disorders.