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

arXiv: https://arxiv.org/abs/2212.11152 · HTML: https://mlsanity.com/paper/2212.11152/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 10 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [Multiview Transformers for Video Recognition](https://mlsanity.com/paper/2201.04288/) (2022)
- [The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose](https://mlsanity.com/paper/2007.00394/) (2020)
- [Deep ConvLSTM with self-attention for human activity decoding using wearables](https://mlsanity.com/paper/2005.00698/) (2020)

## Built on by (most cited first, 11 of 11)

- [Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUs](https://mlsanity.com/paper/2406.01316/) (2024)
- [TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines](https://mlsanity.com/paper/2408.14146/) (2024)
- [iSafetyBench: A video-language benchmark for safety in industrial environment](https://mlsanity.com/paper/2508.00399/) (2025)
- [Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation](https://mlsanity.com/paper/2503.15126/) (2025)
- [OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments](https://mlsanity.com/paper/2603.02390/) (2026)
- [SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation](https://mlsanity.com/paper/2311.17428/) (2023)
- [Self-supervised New Activity Detection in Sensor-based Smart Environments](https://mlsanity.com/paper/2401.10288/) (2024)
- [AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild](https://mlsanity.com/paper/2605.22715/) (2026)
- [SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models](https://mlsanity.com/paper/2507.06405/) (2025)
- [Spectral Scalpel: Amplifying Adjacent Action Discrepancy via Frequency-Selective Filtering for Skeleton-Based Action Segmentation](https://mlsanity.com/paper/2603.24134/) (2026)
- [GSO-Net: Visual State Machines for Hazardous Freight Transfer Compliance at Petrochemical Logistics Nodes](https://mlsanity.com/paper/2609.12408/) (2026)

## Abstract (arXiv)

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 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 acceleration data, keypoints, depth images, and readings from IoT-enabled devices (e.g., handheld barcode scanners), collected from 16 distinct subjects with different levels of packaging work experience. We apply state-of-the-art human activity recognition techniques to the dataset and provide future directions of complex work activity recognition studies in the pervasive computing community based on the results. We believe that OpenPack will contribute to the sensor-based action/activity recognition community by providing challenging tasks. The OpenPack dataset is available at https://open-pack.github.io.