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  • https://doi.org/10.1109/itc-cscc66376.2025.11137728Copy DOI Icon

Predictive Analytics-Driven Wearable System for Personalized Asthma Management in Disabled Patients

  • Jul 7, 2025
  • Uchira Wickramarathne +2 more
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Abstract

Asthma is a chronic respiratory condition requiring continuous monitoring to prevent severe exacerbations. Traditional management lacks real-time data integration and predictive capabilities. This research introduces an innovative predictive analytics-driven system that enhances asthma management for disabled patients by integrating a wearable device, an inhaler (metered-dose inhaler) and a mobile application. The wearable device, equipped with an MQ135 air quality sensor, DHT11 temperature and humidity sensor and a MAX30100 SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and heart rate sensor, continuously monitors environmental factors including air quality, humidity, temperature and physiological parameters including heart rate, SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> levels. The inhaler’s touch sensor tracks its usage status (ON/OFF) to ensure proper adherence. A linear regression-based model analyzes these real-time data streams and achieves an accuracy of 0.92 and a mean squared error (MSE) of 0.08, to predict asthma attacks and sends personalized alerts for proactive inhaler use. By integrating real-time environmental and physiological data, this system offers a personalized and data-driven approach to asthma management, significantly improving patient outcomes.

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