Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5
Text Correction
CNN-Blocks | IMU-Specific (Train/Validation/Test) | Channel-Specific (Train/Validation/Test) | Baseline (Train/Validation/Test) |
1 | 0.945/0.951/0.891 | 0.96/0.96/0.9 | 0.936/0.956/0.894 |
2 | 0.96/0.958/0.9 | 0.959/0.948/0.896 | 0.973/0.959/0.881 |
3 | 0.954/0.956/0.896 | 0.935/0.949/0.877 | 0.952/0.953/0.901 |
Dataset | Training Set | Validation Set | Test Set |
Hurdle Step | 0.686 ± 0.045 | 0.679 ± 0.049 | 0.645 ± 0.049 |
Hurdle Step right | 0.729 ± 0.037 | 0.755 ± 0.062 | 0.687 ± 0.041 |
Hurdle Step left | 0.582 ± 0.041 | 0.566 ± 0.019 | 0.546 ± 0.018 |
Inline Lunge | 0.877 ± 0.037 | 0.862 ± 0.05 | 0.825 ± 0.023 |
Inline Lunge right | 0.863 ± 0.044 | 0.815 ± 0.062 | 0.84 ± 0.037 |
Inline Lunge left | 0.868 ± 0.012 | 0.846 ± 0.05 | 0.849 ± 0.046 |
Trunk Stability Pushup | 0.953 ± 0.027 | 0.897 ± 0.043 | 0.914 ± 0.037 |
Deep Squat | 0.941 ± 0.029 | 0.948 ± 0.014 | 0.9 ± 0.021 |
Dataset | Training Set | Validation Set | Test Set |
Hurdle Step | 0.816 ± 0.019 | 0.821 ± 0.015 | 0.301 ± 0.284 |
Hurdle Step right | 0.854 ± 0.04 | 0.792 ± 0.021 | 0.267 ± 0.258 |
Hurdle Step left | 0.821 ± 0.019 | 0.868 ± 0.022 | 0.405 ± 0.409 |
Inline Lunge | 0.912 ± 0.031 | 0.88 ± 0.026 | 0.331 ± 0.177 |
Inline Lunge right | 0.859 ± 0.03 | 0.806 ± 0.017 | 0.442 ± 0.353 |
Inline Lunge left | 0.884 ± 0.026 | 0.813 ± 0.036 | 0.498 ± 0.347 |
Trunk Stability Pushup | 0.953 ± 0.022 | 0.954 ± 0.01 | 0.154 ± 0.318 |
Deep Squat | 0.978 ± 0.01 | 0.953 ± 0.007 | 0.485 ± 0.427 |
Reference
- Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Spilz, A.; Munz, M. Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5. Sensors 2025, 25, 4110. https://doi.org/10.3390/s25134110
Spilz A, Munz M. Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5. Sensors. 2025; 25(13):4110. https://doi.org/10.3390/s25134110
Chicago/Turabian StyleSpilz, Andreas, and Michael Munz. 2025. "Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5" Sensors 25, no. 13: 4110. https://doi.org/10.3390/s25134110
APA StyleSpilz, A., & Munz, M. (2025). Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5. Sensors, 25(13), 4110. https://doi.org/10.3390/s25134110