Building a Decision Support System for Automated Mobile Asthma Monitoring in Remote Areas

• URL: http://arxiv.org/abs/2112.11195v1
• Date: Sat, 11 Dec 2021 14:18:08 GMT
• Title: Building a Decision Support System for Automated Mobile Asthma Monitoring in Remote Areas
• Authors: Chinazunwa Uwaoma, Gunjan Mansingh
• Abstract summary: This paper proposes the use of smartphone equipped with embedded sensors, to capture and analyze early symptoms of asthma triggered by exercise. Preliminary results show that smartphones can be used to monitor and detect asthma symptoms without other networked devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Advances in mobile computing have paved the way for the development of several health applications using smartphone as a platform for data acquisition, analysis and presentation. Such areas where mhealth systems have been extensively deployed include monitoring of long term health conditions like Cardio Vascular Diseases and pulmonary disorders, as well as detection of changes from baseline measurements of such conditions. Asthma is one of the respiratory conditions with growing concern across the globe due to the economic, social and emotional burden associated with the ailment. The management and control of asthma can be improved by consistent monitoring of the condition in realtime since attack could occur anytime and anywhere. This paper proposes the use of smartphone equipped with embedded sensors, to capture and analyze early symptoms of asthma triggered by exercise. The system design is based on Decision Support System techniques for measuring and analyzing the level and type of patients physical activity as well as weather conditions that predispose asthma attack. Preliminary results show that smartphones can be used to monitor and detect asthma symptoms without other networked devices. This would enhance the usability of the health system while ensuring users data privacy, and reducing the overall cost of system deployment. Further, the proposed system can serve as a handy tool for a quick medical response for asthmatics in low income countries where there are limited access to specialized medical devices and shortages of health professionals. Development of such monitoring systems signals a positive response to lessen the global burden of asthma.

Related papers

• Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment [45.104212062055424]
  This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), were trained to predict breathing types.
  arXiv  Detail & Related papers  (2024-09-05T02:14:31Z)
• Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery [1.03590082373586]
  The review highlights various types of mHealth applications used for remote monitoring, such as telemedicine platforms, mobile apps for chronic disease management, and wearable devices. The benefits of these applications include improved patient outcomes, increased access to healthcare, reduced healthcare costs, and addressing healthcare disparities. However, challenges and limitations, such as privacy and security concerns, lack of technical infrastructure, regulatory is-sues, data accuracy, user adherence, and the digital divide, need to be addressed.
  arXiv  Detail & Related papers  (2024-08-26T11:32:43Z)
• Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems [69.41229290253605]
  Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data. We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
  arXiv  Detail & Related papers  (2024-01-19T16:26:35Z)
• IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic [0.0]
  COVID19 and asthma are respiratory diseases that can be life threatening in uncontrolled circumstances. A poverty stricken South Asian country like Bangladesh has been bearing the brunt of the COVID19 pandemic since its beginning. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system.
  arXiv  Detail & Related papers  (2023-03-28T07:23:03Z)
• PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation [54.839600943189915]
  Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks.
  arXiv  Detail & Related papers  (2022-12-14T21:39:15Z)
• Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones [75.23250968928578]
  We present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset. The proposed method shows promising results in predicting three medication statuses objectively.
  arXiv  Detail & Related papers  (2022-07-26T02:08:08Z)
• Certainty Modeling of a Decision Support System for Mobile Monitoring of Exercise induced Respiratory Conditions [0.0]
  The aim is to develop a mobile tool to assist patients in managing their conditions. We present the proposed model architecture and then describe an application scenario in a clinical setting.
  arXiv  Detail & Related papers  (2021-10-15T07:26:36Z)
• Wearable Health Monitoring System for Older Adults in a Smart Home Environment [1.14219428942199]
  We present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. A voice-based prototype is also implemented and the feasibility of the proposed system for integration in a smart home environment is analyzed.
  arXiv  Detail & Related papers  (2021-06-09T03:16:54Z)
• Smart Speakers, the Next Frontier in Computational Health [0.0]
  Smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research. The recent rise and ubiquity of these smart computing systems hold significant potential for enhancing chronic disease management. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health.
  arXiv  Detail & Related papers  (2021-03-06T23:13:02Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.