An Open, Labeled Dataset for Analysis and Assessment of Human Motion
| Autor: | Andre Ebert, Adrian Klein, Christian Ungnadner, Chadly Marouane |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Human motion Motion (physics) Activity recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319985503 MobiHealth |
| DOI: | 10.1007/978-3-319-98551-0_12 |
| Popis: | Analysis of human activity, e.g., by tracking and analyzing motion information or vital signs became lots of attention in medical as well as athletic appliances during the last years. Nonetheless, comprehensive and labeled datasets containing human motion information are only sparsely accessible to the public. Especially qualitatively labeled datasets are rare, although they are of great value for the development of concepts concerning qualitative motion assessment, e.g., to avoid injuries during athletic workouts or to optimize a training’s success. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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