Deep Neural Network based Human Activity Recognition for the Order Picking Process
Abstract
Although the fourth industrial revolution is already in pro-gress and advances have been made in automating factories, completely automated facilities are still far in the future. Human work is still an important factor in many factories and warehouses, especially in the field of logistics. Manual processes are, therefore, often subject to optimization efforts. In order to aid these optimization efforts, methods like human activity recognition (HAR) became of increasing interest in industrial settings. In this work a novel deep neural network architecture for HAR is introduced. A convolutional neural network (CNN), which employs temporal convolutions, is applied to the sequential data of multiple intertial measurement units (IMUs). The network is designed to separately handle different sensor values and IMUs, joining the information step-by-step within the architecture. An evaluation is performed using data from the order picking process recorded in two different warehouses. The influence of different design choices in the network architecture, as well as pre- and post-processing, will be evaluated. Crucial steps for learning a good classification network for the task of HAR in a complex industrial setting will be shown. Ultimately, it can be shown that traditional approaches based on statistical features as well as recent CNN architectures are outperformed.
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Grzeszick et al. proposed a multi-branch CNN architecture to account for different types of sensors and activities.
“To account for all these different types of sensors and activities, Grzeszick et al. [176] proposed a multi-branch CNN architecture.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [176]
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Angulo, A., Beltrán, J., & Castro, L. A. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610735
Appelhans, H., Feldmann, C., & Borgmann, C. (2024) · Lecture notes in logistics · doi:10.1007/978-3-031-56826-8_18
Enhancing Human Activity Recognition with IMU-based Motion Analysis
Singh, A., Verma, A., & Cherukuri, B. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541396
Feature representation learning for human activity recognition
Khaertdinov, B. (2024) · doi:10.26481/dis.20240618bk
Mensch-Technik-Interaktion und Social Networked Industry
Avsar, H., Reining, C., & Feldmann, F. (2024) · Springer eBooks · doi:10.1007/978-3-642-54476-7_33-1
Aging Challenges and Smart Living
Sheng, Q. Z., Liu, Y., & Yao, L. (2026) · Springer eBooks · doi:10.1007/978-3-032-16502-2_2
Large Language Models Meet Wearable Sensing: A Demo of Training-Free HAR with RAG-HAR
Karunarathna, H., Sivaroopan, N., Madarasingha, C., Jayasumana, A. P., & Thilakarathna, K. (2026) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops68308.2026.11585308
- Authors
- Grzeszick, R., Lenk, J. M., Rueda, F. M., Fink, G. A., Feldhorst, S., & ten Hompel, M.
- Affiliations
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
- TU Dortmund University (DE)
- Fraunhofer IML / Department of Mechanical Engineering, TU Dortmund University, Dortmund, Germany
- Venue
- Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction
- Publisher
- ACM
- Published
- 2017-09-21
- Pages
- 1-6
- OpenAlex
- W2770106921
- Cited by
- 93
- References
- 13
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 16 · 2023: 12 · 2022: 19 · 2021: 13 · 2020: 8 · 2019: 9 · 2018: 7
- FWCI
- 1.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (Fi799/10-1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human activity recognition, order picking, warehouse logistics, convolutional neural networks, temporal convolution, inertial measurement unit, sensor fusion, deep learning architectures, industrial automation
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