Context-Aware Cyber-Physical Assistance Systems in Industrial Systems: A Human Activity Recognition Approach

The increasing demand for product customisation is leading to higher complexities within manufacturing. This imposes new challenges for the workforce. One way to support operators' productivity may be context-aware, human-centred cyber-physical assistance systems. Human Activity Recognition (HA...

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Bibliographic Details
Published in2020 IEEE International Conference on Human-Machine Systems (ICHMS) pp. 1 - 6
Main Authors Roth, Elisa, Moncks, Mirco, Bohne, Thomas, Pumplun, Luisa
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
Subjects
Activity recognition
Atmospheric measurements
Classification algorithms
Context-aware systems
Human Activity Recognition
Manufacturing
Manufacturing Activity Dataset
Methods-Time Measurement
Particle measurements
Physical Assistance
Task analysis
Visualization
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Summary:The increasing demand for product customisation is leading to higher complexities within manufacturing. This imposes new challenges for the workforce. One way to support operators' productivity may be context-aware, human-centred cyber-physical assistance systems. Human Activity Recognition (HAR) is a promising approach to enable context-awareness. However, standardised approaches to integrate HAR into existing manufacturing environments are rare. Particularly, there is a lack of available datasets of manufacturing activities. Moreover, comparative studies of inertial and visual HAR approaches are still rare. This work therefore proposes Methods-Time Measurement (MTM) as a standardised foundation for creating a manufacturing activity dataset. Subsequently, five different machine learning algorithms are tested for their recognition performance based on the dataset captured with an inertial sensor suit and an RGB-D sensor. A proof-of-concept is delivered for both sensor categories applied to the scope of 18 MTM-1 activities, whereas inertial data outperformed depth data. K-Nearest Neighbour and Bagged Tree algorithms revealed the best classification accuracy results in this context.
DOI:10.1109/ICHMS49158.2020.9209488