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Activity recognition in manual manufacturing: Detecting screwing processes from sensor data

Year: 2019

Type: article

Abstract: Knowledge about the duration of manufacturing processes and operation times is essential for production planning and control. But data acquisition is often difficult and especially challenging if production requires manual activities. This paper presents different data analysis and machine learning approaches to detect manual manufacturing processes from sensor data. As human activity recognition approaches are not necessarily applicable in industrial environments, all sensors are attached to tools, in this case screwdrivers. A dataset covering different tool movements, sensor types and mounting options is created and analyzed. The results are evaluated in terms of feasibility of the approach.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2019.03.288

Authors: Lisa C. Günther, Susann Kärcher, Thomas Bauernhansl

Institution: Fraunhofer Institute for Manufacturing Engineering and Automation

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2955224946

FWCI: 0.896

Cites: 35

Cited by: 16

Related to: 10