A Tensor‐based Regression Approach for Human Motion Prediction
Open accessAbstract
Collaborative robotic systems will be a key enabling technology for current and future industrial applications. The main aspect of such applications is to guarantee safety for humans. To detect hazardous situations, current commercially available robotic systems rely on direct physical contact to the co‐working person. To further advance this technology, there are multiple efforts to develop predictive capabilities for such systems. Using motion tracking sensors and pose estimation systems combined with adequate predictive models, potential episodes of hazardous collisions between humans and robots can be predicted. Based on the provided predictive information, the robotic system can avoid physical contact by adjusting speed or position. A potential approach for such systems is to perform human motion prediction with machine learning methods like artificial neural networks (NNs). In our approach, the motion patterns of past seconds are used to predict future ones by applying a linear Tensor‐on‐Tensor Regression model, selected according to a similarity measure between motion sequences obtained by dynamic time warping (DTW). For test and validation of our proposed approach, industrial pseudo assembly tasks were recorded with a motion capture system, providing unique traceable Cartesian coordinates for each human joint. The prediction of repetitive human motions associated with assembly tasks, whose data vary significantly in length and have highly correlated variables, has been achieved in real time.
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GAN synthesizes data via adversarial generation, alleviates data scarcity and enables domain adaptation, and is used for sEMG data augmentation and cross-device adaptation.
“| GAN | Synthesize data via adversarial generation | Alleviate data scarcity, enable domain | sEMG data augmentation, cross-device |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [267]
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Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation
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The ENBIS‐21 quality and reliability engineering international special issue
Bischoff, J., & Lepore, A. (2023) · Quality and Reliability Engineering International · doi:10.1002/qre.3266
Uddin, M. S. (2025) · Discover Robotics · doi:10.1007/s44430-025-00014-0
- Authors
- Gril, L., Wedenig, P., Torkar, C., & Kleb, U.
- Affiliations
- Joanneum Research (AT)
- Corresponding author
- Gril, Lorena
- Venue
- Quality and Reliability Engineering International
- Publisher
- Wiley
- Published
- 2022-06-21
- Volume
- 39
- Issue
- 2
- Pages
- 481-499
- ISSN
- 0748-8017 (ISSN-L)
- 1099-1638
- OpenAlex
- W4283203971
- Cited by
- 7
- References
- 56
- Citations by year
- 2026: 1 · 2025: 3 · 2023: 3
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Quality and Reliability Engineering International (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- human motion prediction, collaborative robots, dynamic time warping, motion capture, real-time prediction
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