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Part 1: Document Description
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Citation |
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Title: |
Sit-To Stand Video with Landmark Dataset |
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Identification Number: |
doi:10.34820/FK2/VAEGNR |
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Distributor: |
Telkom University Dataverse |
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Date of Distribution: |
2024-10-28 |
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Version: |
1 |
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Bibliographic Citation: |
Rizal, Achmad; Erfianto, Bayu; Hadiyoso, Sugondo; Istiqomah, Istiqomah, 2024, "Sit-To Stand Video with Landmark Dataset", https://doi.org/10.34820/FK2/VAEGNR, Telkom University Dataverse, V1 |
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Citation |
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Title: |
Sit-To Stand Video with Landmark Dataset |
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Identification Number: |
doi:10.34820/FK2/VAEGNR |
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Authoring Entity: |
Rizal, Achmad (Telkom University) |
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Erfianto, Bayu (Telkom University) |
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Hadiyoso, Sugondo (Telkom University) |
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Istiqomah, Istiqomah (Telkom University) |
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Distributor: |
Telkom University Dataverse |
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Access Authority: |
Rizal, Achmad |
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Depositor: |
Rizal, Achmad |
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Date of Deposit: |
2024-10-28 |
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Study Scope |
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Keywords: |
Computer and Information Science, Medicine, Health and Life Sciences, sit-to-stand, PoseNet |
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Abstract: |
The following is a video data collection on healthy subjects' sit-to-stand transition (STS). Each subject consists of 2 videos, namely the subject before and after the exercise. It is expected that after doing the exercise, the subject will experience fatigue in his knees so that the inconsistency of the knee trajectory can be observed. Landmarking has been done in the video so that the joints involved in the STS can be seen. In addition to the video, STS landmarks are provided as Excel files to facilitate signal analysis. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.3390/ijerph20053879 |
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Bibliographic Citation: |
Erfianto, B., Rizal, A., & Hadiyoso, S. (2023). Empirical mode decomposition and Hilbert spectrum for abnormality detection in normal and abnormal walking transitions. International Journal of Environmental Research and Public Health, 20(5), 3879. |
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