Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis
| Autor: | Dana Kulic, Michelle Karg, Jonathan Feng-Shun Lin |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Computer science Scale-space segmentation Human Factors and Ergonomics 02 engineering and technology Machine learning computer.software_genre 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Segmentation Segmentation-based object categorization business.industry Image segmentation Computer Science Applications Human-Computer Interaction Identification (information) Statistical classification Control and Systems Engineering Gesture recognition Signal Processing 020201 artificial intelligence & image processing Algorithm design Artificial intelligence business computer |
| Zdroj: | IEEE Transactions on Human-Machine Systems. 46:325-339 |
| ISSN: | 2168-2305 2168-2291 |
| DOI: | 10.1109/thms.2015.2493536 |
| Popis: | Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives , to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, human–machine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested. |
| Databáze: | OpenAIRE |
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