| Autor: |
Dill Sebastian, Rösch Andreas, Rohr Maurice, Güney Gökhan, De Witte Luisa, Schwartz Elias, Hoog Antink Christoph |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 563-566 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2364-5504 |
| DOI: |
10.1515/cdbme-2023-1141 |
| Popis: |
With the recent increase in interest in machine learning and computer vision, camera-based pose estimation has emerged as a promising new technology. One of the most popular libraries for camera-based pose estimation is MediaPipe Pose due to its computational efficiency, ease of use, and the fact that it is open-source. However, little work has been performed to establish how accurate the library is and whether it is suitable for usage in, for example, physical therapy. This paper aims to provide an initial assessment of this. We find that the pose estimation is highly dependent on the camera’s viewing angle as well as the performed exercise. While high accuracy can be achieved under optimal conditions, the accuracy quickly decreases when the conditions are less favourable. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
