RespiraTech: Intelligent IoT and AI-Based Breath Analyzer for Early Disease Prediction in Healthcare

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@article{1724469,
      author = {Mahamad Gouse N, Kezia Mary, Rakshitha Kanchan, Vidyashree, Fayaz Ahamed Shaikh},
      title = {RespiraTech: Intelligent IoT and AI-Based Breath Analyzer for Early Disease Prediction in Healthcare},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {4814-4825},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724469.pdf},
      abstract = {This paper describes RespiraTech, a prototype multimodal breath monitoring system based on ESP32, to be used as a platform for further research in healthcare screening. The platform consists of an SGP30 TVOC/eCO₂ sensor, an MQ-135 metal oxide sensor, a DHT22 temperature and humidity sensor, an INMP441 I²S microphone and a MAX30102 pulse oximeter combined with a Python/FastAPI backend, SQLite storage, a web dashboard, and PDF report generator. The contribution of our paper is of methodological rather than diagnostic nature. It includes a specification of capabilities and limitations of each channel, a provenance scheme distinguishing between hardware and synthesized recordings, validity criteria for saturated, zero, and missing channels, and a dataset preparation procedure including a train-test split by participants. We also present honest benchmark results: unstable behavior of SGP30 and MAX30102 sensors, a reading of the MQ-135 sensor at ADC full scale, and a problem with the ESP32 ADC driver interfering with integrated firmware operation.},
      keywords = {Breath analysis; Electronic nose; ESP32; Internet of Things; Multimodal sensing},
      month = {October},
  }