Literature Database Entry

bhattacharjee2025breath

Sunasheer Bhattacharjee, Saswati Pal, Peter Scheepers and Falko Dressler, "Breath Patterns as Signals: A Machine Learning-based Molecular Communication Perspective," Proceedings of 12th ACM International Conference on Nanoscale Computing and Communication (NANOCOM 2025), Chengdu, China, October 2025, pp. 22–27.

Abstract

Molecular communication is a core pillar of the Internet of Bio-Nano Things. Exhaled breath, rich in water vapor, offers a viable medium for air-based molecular communication. This paper presents a low-cost, non-invasive approach using a DHT22 sensor to classify breath patterns, namely Eupnea, Bradypnea, and Tachypnea. Humidity and temperature signals from the mouth and nose are processed using machine learning (ML). The model achieves strong classification performance, showing that ML can effectively distinguish breath patterns despite sensor constraints.

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Sunasheer Bhattacharjee
Saswati Pal
Peter Scheepers
Falko Dressler

BibTeX reference

@inproceedings{bhattacharjee2025breath,
    author = {Bhattacharjee, Sunasheer and Pal, Saswati and Scheepers, Peter and Dressler, Falko},
    doi = {10.1145/3760544.3764127},
    title = {{Breath Patterns as Signals: A Machine Learning-based Molecular Communication Perspective}},
    pages = {22--27},
    publisher = {ACM},
    address = {Chengdu, China},
    booktitle = {12th ACM International Conference on Nanoscale Computing and Communication (NANOCOM 2025)},
    month = {10},
    year = {2025},
   }
   
   

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Last modified: 2025-11-10