Abstract
This work presents an investigation of indoor air pollution using a combination of biomedical engineering (BME) sensors and indoor air sensors. Indoor air quality is a significant factor affecting human health, and monitoring and controlling indoor air pollution have become essential for indoor environ- mental quality management. In this study, BME techniques are employed to analyze indoor air pollutants, and an indoor air sensor is used to measure and monitor the indoor air quality in real-time. The study involves the deployment of a network of sensors in an indoor environment, data collection, and analysis using machine learning algorithms. The results show that the combination of BME and indoor air sensors is an effective approach for detecting and mitigating indoor air pollution, and provides valuable insights for the development of indoor air quality management systems. This study contributes to the growing body of research on indoor air quality and provides new strategies for improving indoor air quality and safeguarding human health.
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