TITLE: OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments 

ABSTRACT: Unlike human daily activities, existing publicly available sensor datasets
for work activity recognition in industrial domains are limited by difficulties
in collecting realistic data as close collaboration with industrial sites is
required. This also limits research on and development of AI methods for
industrial applications. To address these challenges and contribute to research
on machine recognition of work activities in industrial domains, in this study,
we introduce a new large-scale dataset for packaging work recognition called
OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including
keypoints, depth images, acceleration data, and readings from IoT-enabled
devices (e.g., handheld barcode scanners used in work procedures), collected
from 16 distinct subjects with different levels of packaging work experience.
On the basis of this dataset, we propose a neural network model designed to
recognize work activities, which efficiently fuses sensor data and readings
from IoT-enabled devices by processing them within different streams in a
ladder-shaped architecture, and the experiment showed the effectiveness of the
architecture. We believe that OpenPack will contribute to the community of
action/activity recognition with sensors. OpenPack dataset is available at
https://open-pack.github.io/.
Unlike human daily activities, existing publicly available sensor datasets
for work activity recognition in industrial domains are limited by difficulties
in collecting realistic data as close collaboration with industrial sites is
required. This also limits research on and development of AI methods for
industrial applications. To address these challenges and contribute to research
on machine recognition of work activities in industrial domains, in this study,
we introduce a new large-scale dataset for packaging work recognition called
OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including
keypoints, depth images, acceleration data, and readings from IoT-enabled
devices (e.g., handheld barcode scanners used in work procedures), collected
from 16 distinct subjects with different levels of packaging work experience.
On the basis of this dataset, we propose a neural network model designed to
recognize work activities, which efficiently fuses sensor data and readings
from IoT-enabled devices by processing them within different streams in a
ladder-shaped architecture, and the experiment showed the effectiveness of the
architecture. We believe that OpenPack will contribute to the community of
action/activity recognition with sensors. OpenPack dataset is available at
https://open-pack.github.io/.
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments
http://arxiv.org/abs/2212.11152v1