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Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks
Gabriele Bleser1, Dima Damen2, Ardhendu Behera3
1Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany; Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany.
Plos One
|July 1, 2015
Summary
This study introduces a smart assistance system that learns industrial workflows from experts and teaches them to novices using an on-body sensor network and augmented reality. The system enhances worker efficiency and safety without external equipment.
Area of Science:
- Human-Computer Interaction
- Robotics
- Industrial Engineering
Background:
- Industrial assembly and production workflows are increasingly complex, demanding significant cognitive and physical load on workers, especially for infrequent or repetitive tasks.
- Smart assistance systems offer a potential solution to alleviate worker burden and improve task performance and safety.
Purpose of the Study:
- To present a scalable concept and integrated system demonstrator for smart industrial workflow assistance.
- To enable learning of workflows from expert observation and transfer them to novice users.
- To develop and evaluate algorithms for robust user tracking, workspace mapping, object capture, workflow monitoring, and context-sensitive AR feedback.
Main Methods:
- Fusion of inertial and visual sensor data from an on-body sensor network (BSN) for robust pose tracking in challenging environments.
- Learning-based computer vision for workspace mapping, sensor localization, and object capture, even when carried.
- Domain-independent workflow recovery and monitoring using spatiotemporal relations derived from movement data.
- Context-sensitive augmented reality (AR) feedback via a head-mounted display (HMD).
Main Results:
- A prototype system was developed combining state-of-the-art hardware and software, with a focus on interoperability.
- All developed algorithms operate solely on data from the on-body sensor network, eliminating the need for external instrumentation.
- Feasibility demonstrated on three increasingly complex datasets representing manual industrial tasks, highlighting the system's potential and limitations.
Conclusions:
- The proposed learning-based system offers a scalable and adaptable approach to industrial workflow assistance across various tasks and domains.
- The integrated action-perception-feedback loop, powered by an on-body sensor network and AR, shows promise for improving worker performance and safety.
- Further development and testing on larger datasets are needed to fully realize the system's potential and address identified limitations.
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