This paper proposes a real-time framework for an active Smart Odor-Sensing Wearable that integrates high-sensitivity electrochemical gas sensors with edge-computing algorithms and a micro-actuation spraying mechanism. The system autonomously identifies gas anomalies, calculates severity thresholds, and deploys a precise burst of odor neutralizer to actively neutralize flatulence odor before it permeates outer clothing layers. The work presents the system architecture divided into three interconnected layers (Sensor Layer, Edge Processing Layer, Actuation Layer), a dynamic-baseline thresholding mathematical model with exponential moving average (EMA) filtering, a physical minimum-viable- product (MVP) enclosure design validating buildability within a 60mm × 80mm × 30mm form factor (~100g total weight), simulated system performance expectations with ~510ms end-to-end latency, and mobile Bluetooth telemetry integration. All component specifications (MQ-135 electrochemical sensor, Arduino Nano microcontroller, HC-05 Bluetooth module, IRFZ44N MOSFET driver, zinc ricinoleate neutralizer) are grounded in manufacturer datasheets and prior literature. The paper explicitly frames prototype dimensions and operating parameters as MVP design targets rather than measurements from controlled bench-validation, and it identifies three key areas requiring empirical validation before a production revision: (1) real measured latency and sensor response curves, (2) TinyML classifier integration requiring migration to ESP32-class processors, and (3) resolution of the IRFZ44N gate-drive limitation. The framework is positioned as a novel IoT approach to personal hygiene that shifts from passive carbon filtration to active, algorithmic neutralization.
Jeeva N Jeeva N (Fri,) studied this question.