Work

TinyML: Accelerometer-Based Processing for Seismic Detection in Buildings

Year: 2025

Type: conference-paper

Abstract: During emergencies, fast and accurate situational awareness of building collapses supports critical decisions that can save human lives. To tackle this problem, we develop a compact on-device learning model (TinyML) that infers seismic activity from accelerometer readings collected in buildings. Indeed, we explore classification approaches, including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (kNN), and Decision Tree (DT) to detect anomalies in buildings. We also provide a performance comparison using the Designsafe-CI dataset. Our simulation results revealed that both kNN and DT achieved the highest scores among the models for predicting building anomalies, with accuracies of up to 98%. Given these outcomes and the models’ computational footprint, we selected the DT model for the final deployment on the STM32 microcontroller.

DOI: 10.1109/esai67033.2025.11438662

Authors: Marwane Rezzouki, Kaoutar El Hina, Guillaume Terrasson

Institutions: Centre National de la Recherche Scientifique, Laboratoire des signaux et systèmes, CentraleSupélec, École Supérieure des Technologies Industrielles Avancées

Language: English

Topic: Seismology and Earthquake Studies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7140164035

FWCI:

Cites: 16

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