  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.
