An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware

TL;DR

This paper presents an embedded IoT system that autonomously collects and labels sensor data, significantly improving data acquisition efficiency for event detection tasks with high accuracy.

Contribution

It introduces a novel hardware-software integrated system with local labeling capabilities, reducing reliance on external resources and streamlining sensor data collection and annotation.

Findings

Achieved up to 91.67% classification accuracy

Successfully collected and labeled audio and vibration data

Validated with 4-fold cross-validation

Abstract

Recent advancements in IoT technologies have underscored the importance of using sensor data to understand environmental contexts effectively. This paper introduces a novel embedded system designed to autonomously label sensor data directly on IoT devices, thereby enhancing the efficiency of data collection methods. We present an integrated hardware and software solution equipped with specialized labeling sensors that streamline the capture and labeling of diverse types of sensor data. By implementing local processing with lightweight labeling methods, our system minimizes the need for extensive data transmission and reduces dependence on external resources. Experimental validation with collected data and a Convolutional Neural Network model achieved a high classification accuracy of up to 91.67%, as confirmed through 4-fold cross-validation. These results demonstrate the system's robust capability to collect audio and vibration data with correct labels.