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Dr Indrakshi Dey, Head of the Programmable Autonomous Systems Division at Walton Institute, was part of the team whose article ‘Towards Assessing Reliability of Next-Generation Internet of Things Dashboard for Anxiety Risk Classification’ was recently accepted and published in the presitgious IET Wireless Sensor Systems journal.
The paper presents the development and validation of “X-DASH,” a next-generation IoT-based digital dashboard designed for real-time monitoring and classification of anxiety risk levels in patients. The system uses physiological data to assess anxiety, providing over 90% accuracy compared to physician assessments. X-DASH enhances remote healthcare, especially during pandemics, by enabling real-time data-driven decisions, while also analyzing the performance of various IoT communication protocols.
The team worked on creating a smart system called “X-DASH” that helps doctors monitor people’s anxiety levels from afar, especially during challenging times like the COVID-19 pandemic. The system works by using small devices that measure things like heart rate and blood pressure. These measurements are sent to a digital dashboard, where doctors can see if someone is at risk of high anxiety. The goal is to help doctors identify and manage anxiety in patients without them needing to be physically present at a clinic. This system was tested and found to be more than 90% accurate, making it a reliable tool for remote healthcare. It also helps in deciding which technology is best for sending the data quickly and accurately. Overall, X-DASH makes it easier to care for people’s mental health, even from a distance.
Imagine a future where you or your loved ones can have your anxiety levels monitored without needing to visit a doctor’s office. X-DASH, uses simple devices to track your vital signs, sending the data to your doctor in real time. This means that if you’re experiencing high levels of anxiety, your doctor can be alerted immediately and take action, potentially preventing more serious health issues. For people living in remote areas, or those who may have difficulty accessing regular healthcare, this system offers a lifeline. It also helps during pandemics or other crises, where visiting a healthcare facility might not be safe or feasible. Overall, this research could lead to more personalized and proactive healthcare, improving well-being on a broad scale.
The next step for this research involves expanding the use of X-DASH beyond anxiety monitoring to include other health conditions, making it a comprehensive tool for remote healthcare. The system could be enhanced with more advanced sensors and machine learning algorithms to predict future health risks based on historical data. This predictive capability would allow doctors to intervene earlier, potentially preventing serious health issues before they escalate. Additionally, the research aims to test X-DASH on a larger and more diverse population, including people from different geographical regions and healthcare settings, to ensure its effectiveness and generalizability. Integrating X-DASH with existing healthcare systems and ensuring robust data security and patient privacy will also be crucial. Ultimately, the goal is to develop a fully-featured mobile app and cloud-based dashboard that can be widely adopted in hospitals and clinics, bringing cutting-edge remote healthcare to more people globally.
This research has promising industry applications, particularly in healthcare, telemedicine, and wearable technology. Currently, X-DASH can be used by hospitals and clinics to monitor patients’ anxiety levels remotely, reducing the need for in-person visits and allowing for more timely interventions. It’s particularly valuable in managing mental health during pandemics or in areas with limited access to healthcare facilities.
In the future, the technology could be integrated into wearable devices, like smartwatches, to continuously monitor vital signs and assess various health risks in real-time. This could revolutionize chronic disease management, elder care, and personalized medicine by providing continuous, real-time health monitoring. Additionally, the data collected could be used for large-scale health analytics, helping healthcare providers and governments make informed decisions about public health. The integration of this system into existing Internet of Medical Things (IoMT) networks could also enhance the capabilities of smart hospitals and remote patient care services.
Publication Title: Towards Assessing Reliability of Next-Generation Internet of Things Dashboard for Anxiety Risk Classification
Authors: Shama Siddiqui, DHA Suffa University, Pakistan; Anwar Ahmed Khan, Millennium Institute of Technology & Entrepreneurship, Pakistan; Farid Nait Abdesselam, University of Missouri, USA; Shamsul Arfeen Qasmi, Health Security Partners, Pakistan; Adnan Akhundzada, College of Computing & IT, Department of Data and Cybersecurity, University of Doha for Science and Technology, Doha, Qatar; Dr Indrakshi Dey, Walton Institute
Publication Date: August 6, 2024
Name of Journal: IET Wireless Sensor Systems