Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Package monitoring is an important topic in industrial applications, with
significant implications for operational efficiency and ecological
sustainability. In this study, we propose an approach that employs an embedded
system, placed on reusable packages, to detect their state (on a Forklift, in a
Truck, or in an undetermined location). We aim to design a system with a
lifespan of several years, corresponding to the lifespan of reusable packages.
Our analysis demonstrates that maximizing device lifespan requires minimizing
wake time. We propose a pipeline that includes data processing, training, and
evaluation of the deep learning model designed for imbalanced, multiclass time
series data collected from an embedded sensor. The method uses a
one-dimensional Convolutional Neural Network architecture to classify
accelerometer data from the IoT device. Before training, two data augmentation
techniques are tested to solve the imbalance problem of the dataset: the
Synthetic Minority Oversampling TEchnique and the ADAptive SYNthetic sampling
approach. After training, compression techniques are implemented to have a
small model size. On the considered twoclass problem, the methodology yields a
precision of 94.54% for the first class and 95.83% for the second class, while
compression techniques reduce the model size by a factor of four. The trained
model is deployed on the IoT device, where it operates with a power consumption
of 316 mW during inference.
