Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning

TL;DR

This paper presents a deep learning-based embedded system for classifying accelerometer data to monitor industrial packages, emphasizing energy efficiency, data imbalance handling, and model compression for long-term deployment.

Contribution

It introduces a pipeline combining data augmentation, a 1D CNN model, and compression techniques for efficient, accurate, and long-lasting package monitoring with embedded IoT devices.

Findings

Achieved over 94% precision in classifying package states.

Reduced model size by a factor of four through compression.

Deployed the model on IoT device with 316 mW power consumption.

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.