Next Article in Journal
Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning
Previous Article in Journal
Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning
 
 
Correction to Sensors 2023, 23(1), 5.
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Spilz, A.; Munz, M. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. Sensors 2023, 23, 5

Research Group Biomechatronics, University of Applied Sciences Ulm, 89081 Ulm, Germany
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(13), 4110; https://doi.org/10.3390/s25134110
Submission received: 26 March 2025 / Accepted: 7 June 2025 / Published: 1 July 2025
(This article belongs to the Section Biomedical Sensors)

Text Correction

There was an error in the original publication [1]. We would like to adjust the numerical values in Section 3.2. Specifically, we identified a rounding error in the following sentence, which could lead to a misunderstanding regarding our selection of optimal hyperparameters. This rounding issue might create the impression that we determined the best hyperparameters based on test set performance, which is not the case. Instead, we selected the run with the highest validation performance as the optimal configuration.
A correction has been made to Section 3.2:
The optimal parameter combination is selected using the averaged performance from the 5-fold CV. Considered is the averaged macro F1-score achieved on the validation dataset. The best performance was achieved by a network with the following hyperparameters: batch size 32, three CNN-blocks with the “increasing filter, fixed kernel size” scheme, dropout with a rate of 0.2 as a regularization technique, and two LSTM layers. This combination results in a macro F1-score of 0.956 on the training set, 0.955 on the validation set, and 0.906 on the test set. In all tested configurations, macro F1-scores were achieved in the ranges of 0.932–0.969 (training set), 0.939–0.955 (validation set), and 0.867–0.906 (test set).
The Academic Editor has also instructed us to round all other entries of the same metric in the text to 3 decimal places so that this is consistent throughout the publication. Therefore, there are additional corrections in other sections, which I will list in the following.
A correction has been made to Section 3.3:
Regardless of the number of CNN blocks and the CNN structure used, the macro F1-score on training (0.935–0.973), validation (0.948–0.96), and test (0.877–0.901) sets are found to be close to the optimum.
A correction has been made to Section 4.5:
For example, the HS variants achieved a macro F1-score of 0.582–0.729 on the training dataset but a weighted F1-score of 0.821–0.856. Based on the rating distribution of these variants, one can see that rating “1” is underrepresented, and accordingly, a misclassified example of rating “1” influences the macro F1-score significantly more than misclassified examples of the other classes. In contrast, the influence of a misclassified example on the weighted F1-score is independent of the rating. A good example is the macro F1-score of the HS left dataset: The exercise has a score of 0.582 on the training dataset; at the same time, there are hardly any examples for rating “1”, which therefore has a huge impact on the score.
A correction has been made to Section 4.7:
The macro F1-score per exercise is improved by 0.04 (DS), 0.245 (IL), 0.205 (HS), and 0.054 (TSP).
As the majority of instances of the described metric appear in tables, we also have to add the additional third decimal place to three tables:
A correction has been made to Table 2:
CNN-BlocksIMU-Specific
(Train/Validation/Test)
Channel-Specific
(Train/Validation/Test)
Baseline
(Train/Validation/Test)
10.945/0.951/0.8910.96/0.96/0.90.936/0.956/0.894
20.96/0.958/0.90.959/0.948/0.8960.973/0.959/0.881
30.954/0.956/0.8960.935/0.949/0.8770.952/0.953/0.901
A correction has been made to Table 3:
DatasetTraining SetValidation SetTest Set
Hurdle Step0.686 ± 0.0450.679 ± 0.0490.645 ± 0.049
Hurdle Step right0.729 ± 0.0370.755 ± 0.0620.687 ± 0.041
Hurdle Step left0.582 ± 0.0410.566 ± 0.0190.546 ± 0.018
Inline Lunge0.877 ± 0.0370.862 ± 0.050.825 ± 0.023
Inline Lunge right0.863 ± 0.0440.815 ± 0.0620.84 ± 0.037
Inline Lunge left0.868 ± 0.0120.846 ± 0.050.849 ± 0.046
Trunk Stability Pushup0.953 ± 0.0270.897 ± 0.0430.914 ± 0.037
Deep Squat0.941 ± 0.0290.948 ± 0.0140.9 ± 0.021
A correction has been made to Table 4:
DatasetTraining SetValidation SetTest Set
Hurdle Step0.816 ± 0.0190.821 ± 0.0150.301 ± 0.284
Hurdle Step right0.854 ± 0.040.792 ± 0.0210.267 ± 0.258
Hurdle Step left0.821 ± 0.0190.868 ± 0.0220.405 ± 0.409
Inline Lunge0.912 ± 0.0310.88 ± 0.0260.331 ± 0.177
Inline Lunge right0.859 ± 0.030.806 ± 0.0170.442 ± 0.353
Inline Lunge left0.884 ± 0.0260.813 ± 0.0360.498 ± 0.347
Trunk Stability Pushup0.953 ± 0.0220.954 ± 0.010.154 ± 0.318
Deep Squat0.978 ± 0.010.953 ± 0.0070.485 ± 0.427
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. 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.

Share and Cite

MDPI and ACS Style

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

AMA Style

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 Style

Spilz, 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 Style

Spilz, 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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop