A Pervasive Healthcare System for COPD Patients

Monitoring indoor air quality using smart integrated gas sensor module (IGSM) for improving health in COPD patients

Technology advancement, industrialisation, and globalisation have been significant reasons for air pollution outdoors and indoors. It may surprise us that we spend about 80% of our time indoors breathing toxic, stale, polluted air, making us sluggish and fretful. In contrast to outdoor air, indoor air does not recycle consistently. It traps and builds pollutants from wood and coal stoves, furniture and building materials, paints and solvents, cigarette smoke, and cleaning supplies. The prolonged exposure to these hidden pollutants can prompt respiratory disorders such as lung disease, pneumonitis, asthma, pulmonary hypertension, and chronic obstructive pulmonary disease (COPD). It is enduring and is not curable, which has been a threat to humanity for ages. COPD's major cause is airborne particulate matter and other toxic compounds emitted from indoor and outdoor sources. Outdoor air pollution can be controlled only by acquiring changes in the vast population, wherein for indoor, every individual may create a major impact on improving air purity, thereby promoting health. The proposed design model for monitoring indoor air quality was tested in a normal and stimulating environment where we live. Parameters tested included temperature, humidity, amount of PM2.5, and the concentration of CO, CO₂, and NH₃. These parameters were monitored for five to 6 h per day for 8 days. Results indicate that the total air quality lies in the moderate range. Further study will be helpful to utilise this module as an effective Indoor air quality (IAQ) monitoring system. HIGHLIGHTS: • A simple, effective, inexpensive integrated gas sensor module (IGSM) has been proposed in this study to monitor the indoor air quality index (IAQI). • Indoor air quality was tested in a normal and stimulating environment for 8 days. • The integrated gas sensor module (IGSM) was composed of sensors in series, and the outputs for the respective parameter were measured easily. • Among the parameters tested, CO and PM2.5 lie in the moderate range, while other pollutants within the normal range reveal that the tested air quality is moderate.

Virtual mask fitting in pediatric patients during COVID-19: A case series

Introduction

The COVID-19 pandemic has been an unprecedented threat to our health care system. Clinicians had to pivot and develop creative and timely “virtual” solutions to provide clinical care. Our aim was to develop a standardized approach to virtual “mask fitting” for children who are either being initiated or are already on existing long-term ventilation (LTV) at a pediatric hospital.

Case and outcomes

We present three cases involving the care of children who required mask fitting for noninvasive ventilation (NIV). LTV team consultations were delivered via videoconference or phone. With the guidance of the respiratory therapist (RT), the family caregiver (FC) took measurements on their child using a standardized clinical approach (developed by the LTV RTs). Based on the measurements, an appropriate mask was selected. Successful mask fit was based on patient/FC reports, as well as objective leak data obtained from the NIV download data.

Discussion

Virtual clinics used for managing patients in our LTV program were feasible and efficient resulting in improved workflow for the RTs and convenience for patients and FCs. Patients and FCs had significantly less pressure to attend in-person clinics and expressed high satisfaction in terms of their experience and importantly, meeting respiratory care needs. Within the context of COVID-19, remote patient education and intervention can be delivered effectively, while reducing the risk of exposure from in-person visits to hospital.

Conclusion

A virtual/telemedicine program to manage pediatric patients requiring mask fitting for LTV was a feasible option during COVID-19.

A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases

The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.

Context-Aware Healthcare Adaptation Model for COPD Diseases

Nowadays, ubiquitous computing and mobile applications are controlling all our life’s aspects, from social media and entertainment to the very basic needs like commerce, learning, government, and health. These systems have the ability to self-adapt to meet changes in their execution environment and the user’s context. In the healthcare domain, information systems have proven their efficiency, not only by organizing and managing patients’ data and information but also by helping doctors and medical experts in diagnosing disease and taking precluding procedure to avoid serious conditions. In chronic diseases, telemonitoring systems provide a way to monitor the patient’s state and biomarkers within their usual life’s routine. In this article, we are combining the healthcare telemonitoring systems with the context awareness and self-adaptation paradigm to provide a self-adaptive framework architecture for COPD patients.

A Review of Internet of Things Technologies for Ambient Assisted Living Environments

The internet of things (IoT) aims to extend the internet to real-world objects, connecting smart and sensing devices into a global network infrastructure by connecting physical and virtual objects. The IoT has the potential to increase the quality of life of inhabitants and users of intelligent ambient assisted living (AAL) environments. The paper overviews and discusses the IoT technologies and their foreseen impacts and challenges for the AAL domain. The results of this review are summarized as the IoT based gerontechnology acceptance model for the assisted living domain. The model focuses on the acceptance of new technologies by older people and underscores the need for the adoption of the IoT for the AAL domain.