Name
Title | ||
|---|---|---|
Context-Aware Cyber-Physical Assistance Systems in Industrial Systems: A Human Activity Recognition Approach |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICHMS49158.2020.9209488 | 2020 IEEE International Conference on Human-Machine Systems (ICHMS) |
Keywords | DocType | ISBN |
Physical Assistance,Context-aware systems,Human Activity Recognition,Methods-Time Measurement,Manufacturing Activity Dataset | Conference | 978-1-7281-5871-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisa Roth | 1 | 0 | 0.34 |
| Mirco Möncks | 2 | 0 | 0.34 |
| Thomas Bohné | 3 | 0 | 0.34 |
| Luisa Pumplun | 4 | 0 | 0.34 |