Early Fire Warning System for People with Hearing Disabilities Based on IoT and Haptic Signals
DOI:
https://doi.org/10.32870/recibe.v15i2.499Keywords:
IoT, fuzzy logic, Wear OS, fire detection, wearable computingAbstract
This paper describes the development of a hybrid IoT security system designed for early detection of fires. It integrates temperature and humidity (DHT11) and CO (MQ-7) sensors connected to an ESP32, which sends the information to a smartwatch and a smartphone (SMS/Telegram) to alert users with hearing impairments in real time. The method consists of a data acquisition stage (ESP32 board) and a processing stage using a fuzzy inference engine implemented on a TicWatch E3 with Wear OS 3.5. The Kotlin programming language is used to analyze the input variables and figure out the risk level (Low, Medium, High, and Extreme). The system automatically distinguishes between smoke and active fire threats, supported by visual confirmation using the Yolov8s algorithm. Detection results are sent via a Telegram message. The results show that the application can activate haptic feedback and play a video with Mexican Sign Language alerts on the smartwatch when safety thresholds are exceeded. During the testing phase, outstanding results were obtained when applying the precision and accuracy metrics, with a result of 97%, while MAE (Mean Absolute Error) presented a score of 0.0327 in a Monte Carlo simulation of 3000 cases to validate the fuzzy engine, while in the tests in the real environment, the system reacted accurately, with a favorable acceptance by the users who participated in the experiments.References
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