Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study

Dermot Sheridan; Arne Jaspers; Dinh Viet Cuong; Tim Op De Beéck; Niall M. Moyna; Toon T. de Beukelaar; Mark Roantree

doi:10.1371/journal.pone.0319760

Peer Review History

Original SubmissionNovember 8, 2024
8 Nov 2024 Author Response https://doi.org/10.1371/journal.pone.0319760.r001
28 Nov 2024 Decision Letter - Noman Naseer, Editor PONE-D-24-39300Estimating Oxygen Uptake in Simulated Team Sports Using Machine Learning Models and Wearable Sensor DataPLOS ONE Dear Dr. Sheridan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. The paper has been reviewed by two reviewers, and their opinios differ. One has raised serious concerns and recommended rejction whereas the other has raised some concerns and recommended revisions. I have reviewed it myself as well and I think authors should be given a chance to address the reviewers' concerns. Please submit your revised manuscript by Jan 12 2025 11:59PM. 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One has raised serious concerns and recommended rejection whereas the other has raised some concerns and recommended revisions. I have reviewed it myself as well and I think authors should be given a chance to address the reviewers' concerns. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The article is very well-written, with a proper introduction to the topic in the introduction section. I like how the abstract is in a very simplistic manner elaborates on estimation of VO2 data and refers to what the study attempts to do increase its accuracy. The data sample of 6 people, however, is smaller to make generalized conclusions about the Machine Learning models created by the study. Moreover, the authors have systematically presented the problem they are addressing at hand to make assessment of VO2 max easier for athletes. In the methodology, there is a need to include the results obtained from the initial protocols and tests (exercises) performed by the athletes, while they were being selected for the study. Moreover, the measurement setup needs to be explained more. Furthermore, the gyroscope and accelerometer data need to be elaborated for the readers clarity. Within the Machine Learning section, there is a need to include evidences to support use of LOSO cross validation, and grid search technique. Moreover, the authors need justify the use of Bland-Altman analysis and regression analysis. In the results section, the authors need to explain the performance metrics in the text. This section ends abruptly with just explaining the inaccuracies of the previous model. Presently, the discussion section contains ample newer points which could have been part of the results section. The authors need to reconfigure this and orient the sections again. The authors mention in the conclusion section that this is a pilot study and future studies need to configure this model for a wider data set. This notion should also reflect in the title and abstract of the study. Overall, the idea and work seem sound but I would definitely request the authors to revisit and rework on the manuscript based on the given feedback. Reviewer #2: This papers compares several machine learning algorithms for the prediction of the VO2 in 6 athletes, using wearable IMU devices. Whereas the topic is worth investigating, the novelty seems limited, as there are already previous publications doing this prediction, as stated in the manuscript. There are also some signs that the experimental methodology might need to be improved to actually address VO2 prediction, rather than activity detection: - The Leave-One-Subject-Out cross-validation idea is valid, but data from the first session of the test subject was used in the training set. It would be necessary to know the performance of the proposal in a real Leave-One-Subject-Out scenario, where no data from the test subject is used for training, to assess the performance in future, "unregistrated" subjects. - The predicted value appears to follow a strongly bimodal distribution (figs 3 and 5), so the prediction is largely reduced to distinguishing between low and high values of the VO2. This, in combination with the previous comment, leads me to think that the algorithm might actually learn to distinguish between two states (active/inactive) for each athlete, and their corresponding VO2 levels, rather than actually linking movement and VO2. - The abstract is misleading: 8 athletes started the study, but only 6 are actually included in the dataset. - Line 152: the Zephyr data was "processed", but this processing is not described. - PCA is used prior to linear regression to reduce the dimensionality of the data, but there appear to be only 6 features, which would make PCA largely unnecessary. Also, the number of kept dimensions is not mentioned. - The data repository is not provided. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. 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Revision 1
16 Jan 2025 Author Response Reviewer 1 Comments and Responses Comment 1: The article is very well-written, with a proper introduction to the topic in the introduction section. I like how the abstract elaborates on the estimation of VO2 data and refers to what the study attempts to do to increase its accuracy. Response: We thank the reviewer for their positive feedback on the clarity of the introduction and abstract. We are pleased that the objectives and scope of the study were well received. Comment 2: The data sample of 6 people is too small to make generalised conclusions about the Machine Learning models created by the study. Response: We acknowledge the reviewer’s concern regarding the small sample size. This study was designed as a pilot investigation to develop and evaluate the feasibil- ity of using machine learning models for VO2 estimation with wearable sensors. To address this, we have updated the manuscript to explicitly state this in the Meth- ods section (Page 5, Lines 89–91) and the Discussion section (Page 21-22, Lines 484-489). Additionally, we have revised the title and abstract to clearly reflect the pilot nature of the study. Comment 3: Include results obtained from the initial protocols and tests (exercises) performed by the athletes during selection. Response: We appreciate the reviewer’s suggestion to include results from the ini- tial protocols and tests performed during athlete selection. To address this, we 1 have added a summary of the baseline tests, including descriptive statistics for the fitness parameters VO2max collected during the incremental fitness tests. This in- formation has been incorporated into the Methods section (Page 5, Lines 89–91), To provide additional context and detail regarding participant baseline tests, we have included a tables to summarise the results: Table 1 (Page 5) presents individual participant data for VO2max and resting VO2 (VOR). These additions provide a more comprehensive view of the dataset and enhance clarity for the reader. Comment 4: The measurement setup needs to be explained more. Furthermore, the gyroscope and accelerometer data need to be elaborated for the reader’s clarity. Response: We appreciate the reviewer’s suggestion to provide additional details about the measurement setup and the gyroscope and accelerometer data. To ad- dress this, we have expanded the description of the measurement setup in the Meth- ods section (Page 6-7, Lines 134–151). This includes a detailed explanation of the placement and rationale for selecting specific body locations for the inertial measurement units (IMUs), as well as the types of signals recorded (e.g., linear acceleration, angular velocity, magnetic field). Additionally, we described the cal- ibration procedure for the wearable sensors to ensure accurate and reliable data collection. These updates provide greater clarity on the methodology and improve transparency regarding the data acquisition process. Comment 5: Justify the use of LOSO cross-validation and grid search technique. Response: We appreciate the reviewer’s comment regarding the need to provide evidence and justification for the use of LOSO cross-validation and the grid search technique. To address this, we have expanded the Machine Learning section (Page 10, Lines 198–214) to include a detailed rationale for both methodologies. LOSO cross-validation was chosen to mimic real-world scenarios where models must pre- dict outcomes for unseen individuals, ensuring the model’s generalisation capability. Additionally, we described the modified LOSO strategy used in this study, which includes data from the first visit of the test subject during training to capture intra-individual variability while testing on data from the second visit. We have also elaborated on the grid search technique, explaining its role in systematically identifying the optimal hyperparameters (e.g., number of layers and neurons) to minimise RMSE. This ensures a robust and unbiased search across the hyperpa- rameter space and improves model performance. These changes provide additional clarity and context for the methodological choices made in the study. Comment 6: Justify the use of Bland-Altman analysis and regression analysis. Response: We appreciate the reviewer’s comment regarding the justification for the use of Bland-Altman analysis. To address this, we have expanded the Statistics section (Page 14, Lines 301–307) to include a detailed rationale for its inclusion. Specifically, we highlight that Bland-Altman analysis provides a visual represen- tation of the agreement between predicted and measured VO2 values, allowing for the identification of systematic biases and variability across the range of measure- ments. This approach complements traditional metrics such as MAE and RMSE by offering insights into how prediction errors vary with VO2 magnitude, thereby enhancing the interpretability of the model’s performance. These changes clarify the relevance and importance of Bland-Altman analysis in the context of our study. 2 Comment 7: Explain the performance metrics in the results section. The discussion contains points that should be part of the results section; reconfigure and orient the sections. Response: We appreciate the reviewer’s suggestion to improve the explanation of performance metrics and reorganize the Results and Discussion sections. To address this, we have made the following updates: 1. Expanded the Results section (Page 14-15, Lines 309–341) to provide detailed explanations of performance metrics, including RMSE, MAE, and R2, with their relevance to evaluating model performance. This ensures that the metrics are in- terpreted clearly in the context of the study. 2. Introduced Bland-Altman analysis as a complementary evaluation method. We included a justification for its use in assessing systematic bias and agreement be- tween measured and predicted VO2 values, enhancing the depth of the analysis. 3. Reorganised the Results section by grouping findings into distinct themes: - Model performance across configurations. - Evaluation of agreement and prediction bias using Bland-Altman plots. - Residual analysis across exercise conditions. - Temporal predictions to assess the model’s behavior in different phases. 4. Moved relevant points from the Discussion section to the Results section to ensure all findings are cohesively presented. These changes provide a clearer and more structured presentation of the results, aligning with the reviewer’s feedback to improve the flow and comprehensiveness of the manuscript. Comment 8: The notion of this being a pilot study should also reflect in the title and abstract. Response: We thank the reviewer for highlighting the need to consistently convey the pilot nature of the study throughout the manuscript. In response, we have made the following changes: 1. Updated the title to explicitly include the term ”pilot study” to reflect the ex- ploratory nature of this research. The revised title now reads: Estimating Oxygen Uptake in Simulated Team Sports Using Machine Learning Models and Wearable Sensor Data: A Pilot Study (Page 1, Line 1). 2. Revised the abstract to emphasise that this is a pilot study, explicitly mentioning the small sample size and the exploratory intent. The abstract now includes the statement: ”This pilot study investigates the feasibility of using machine learning models to estimate oxygen uptake (VO2) with wearable sensors during team sports activities.”. These revisions ensure that the pilot nature of the study is communi- cated consistently across key sections of the manuscript, aligning with the reviewer’s feedback. Reviewer 2 Comments and Responses Comment 1: The Leave-One-Subject-Out cross-validation idea is valid, but data from the first session of the test subject were used in the training set. A 3 real Leave-One-Subject-Out scenario should exclude all test subject data from the training set. Response: We appreciate the reviewer’s comment regarding the LOSO cross-validation approach. To address this, we have clarified the rationale for employing a modified LOSO strategy: 1. The inclusion of data from the first session of the test subject in the training set reflects a practical scenario in athlete monitoring. Preseason fitness profiles, which are collected routinely, provide a valuable baseline for building predictive models. Leveraging this data allows the model to account for athlete-specific fitness char- acteristics, enabling personalised tracking and monitoring throughout the season. This approach ensures that the model can effectively use preseason data to monitor fitness during subsequent sessions. 2. Although a stricter LOSO approach, excluding all data from the test subject, provides a purer generalisation test, our modified approach was deliberately chosen to align with real-world applications in athlete monitoring systems. This modified strategy, similar to those employed in related studies (e.g., Zignoli et al., 2020), incorporates preseason data to improve model predictions over a season. By using session-level data from the same individual, the model gains insight into intra- individual variability, which is critical for practical applications. 3. This strategy strikes a balance between generalisation and utility, ensuring robust performance while maintaining relevance to practical athlete monitoring scenarios. We have updated the paper (Page 10, Lines 198–210) to highlight the key utility of the modified LOSO approach in leveraging preseason fitness data, aligning it with the practical applications of athlete monitoring systems. Comment 2: The predicted value appears to follow a strongly bimodal distribution, which suggests the algorithm might be distinguishing between states (active/inactive) rather than predicting VO2. Response: We thank the reviewer for raising this important observation about the bimodal distribution in the predicted VO2 values. In response to this comment, we have made the following updates: Inclusion of Kernel Density Estimation Analysis: To address the concern regarding bimodal distribution, we performed a Kernel Density Estimation (KDE) analysis on the predicted VO2 values to visually assess the distribution and better understand the model’s behavior. The KDE results are now presented in Fig- ure 3 (sub-plot c), where the bimodal distribution is clearly illustrated. This addition complements the previously included correlation and Bland-Altman plots and provides a more comprehensive evaluation of the model’s predictive behaviour. Corresponding updates have been made to the text on Page 15, Lines 340–345, to describe this analysis and its implications. Expanded Discussion of Bimodal Distribution as a Limitation: We have acknowledged the observed bimodal distribution as a key limitation in our study and included it in the Discussion section on Page 22, Lines 481–490. Specifically, the limitation highlights that the model primarily distinguishes between active and inactive states, potentially at the expense of capturing finer transitions within active states or between intensities.Future work is suggested to address this limitation by 4 designing improved protocols that incorporate greater variability and transitions, reflecting the dynamic nature of team sports. These additions aim to provide a balanced and transparent discussion of the model’s limitations while suggesting avenues for future improvements. We believe that these updates effectively address the concerns of the reviewer and improve the overall quality and depth of the manuscript. Comment 3: The abstract is misleading: 8 athletes started the study, but only 6 are included in the dataset. Response: We appreciate the reviewer’s observation regarding the discrepancy in the abstract about the number of participants included in the dataset. To address this, we have revised the abstract to accurately reflect that the final dataset consisted of six participants. This change ensures consistency and transparency in reporting the study population. Additionally, we updated the Participants section in the Methods (Page 5, Lines 89–92) to provide a detailed explanation of the reduction in the sample size. These changes clarify the participant inclusion process and align all sections of the manuscript with the final dataset used for analysis. Comment 4: The Zephyr data was ”processed,” but this processing is not described. Response: We appreciate the reviewer’s observation regarding the processing of Zephyr data. To clarify, the Zephyr data (heart rate and breathing rate) was recorded at a frequency of 1 Hz using the device’s built-in software. No additional processing was applied, as the data were directly exported from the device and merged with the breath-by-breath VO2 data collected from the Cosmed K5 system. This merging ensured synchronisation between the datasets for subsequent analysis. We have updated the Methods section (Page 8, Lines 168-169) to include this clari- fication, ensuring that the process is transparently documented for reproducibility. Comment 5: PCA is used prior to linear regression, but there appear to be only 6 features, making PCA largely unnecessary. The number of retained dimensions is also not mentioned. Response: We thank the reviewer for highlighting the potential redundancy of using PCA with a small number of input features. To address this, we have clarified in the Methods section that PCA was included to ensure consistency with preprocessing practices and to represent the input data in an orthogonal space. While PCA was not strictly necessary given the limited feature set, it was applied as a precautionary measure to mitigate potential multicollinearity and align with standard workflows in similar studies. Additionally, we have updated the manuscript to explicitly state that all six prin- cipal components were retained. This decision was based on experimental findings that dimensionality reduction did not improve test scores, and retaining all com- ponents allowed the model to fully leverage the variability in the input data. We have included this clarification in the Methods section on Page 11, Lines 225–232. These updates aim to enhance our methodology and address the reviewer’s concerns effectively. 5 Comment 6: The data repository is not provided. Response: We appreciate the reviewer’s comment regarding the availability of the data. To address this, we have made the complete dataset publicly accessible via Zenodo, ensuring compliance with PLOS ONE’s data-sharing policy. The dataset, along with a detailed description and metadata, can now be accessed at the following DOI: 10.5281/zenodo.14609092. We have updated the Data Availability Statement in the manuscript to reflect this change (Page 16, Lines 528–529), ensuring transparency and reproducibility of the study. Editorial Comments and Responses Comment 1: Include Dr. Niall M. Moyna in the author list. Response: We have updated the manuscript submission data to include Dr. Niall M. Moyna as an author, ensuring that the author list is accurate and complete. Comment 2: Ensure the abstract in the manuscript matches the one in the submission form. Response: We have reviewed and ensured that the abstract in the manuscript matches the one provided in the online submission form. Both versions now ac- curately reflect the updated participant details, key findings, and the pilot nature of the study. This ensures consistency across all submitted materials. Comment 3: Complete and submit the inclusivity questionnaire. Response: We have completed PLOS’ questionnaire on inclusivity in global research and included it as Supporting Information (File attached). This ensures compliance with PLOS ONE’s policy to enhance transparency in reporting research conducted outside the authors’ own country or community. Additionally, we confirm that the manuscript reflects the principles of inclusivity and ethical research practices as outlined in the questionnaire. Comment 4: Ensure data and code are publicly available in a repository. Response: In compliance with PLOS ONE’s data and code-sharing policies, w https://doi.org/10.1371/journal.pone.0319760.r003
7 Feb 2025 Decision Letter - Noman Naseer, Editor Estimating Oxygen Uptake in Simulated Team Sports Using Machine Learning Models and Wearable Sensor Data: A Pilot Study PONE-D-24-39300R1 Dear Dr. Sheridan, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: I have reviewed the amendments done by the authors. They have carefully addressed each remark pointed out in the previous review report. I would like to appreciate their efforts in making this manuscript clear, robust and comprehensive. All the best! Reviewer #3: All the comments and suggestions provided during the review process have been carefully addressed and incorporated into the revised manuscript. We appreciate the valuable feedback, which has helped improve the clarity and quality of the study. As this is a pilot study, we acknowledge that future research can benefit from an expanded dataset with a larger number of subjects to further validate and generalize the findings. We look forward to building upon these results in future work by considering a broader participant pool. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #3: Yes: Jamila Akhter ******** https://doi.org/10.1371/journal.pone.0319760.r004
Formally Accepted
Acceptance Letter - Noman Naseer, Editor PONE-D-24-39300R1 PLOS ONE Dear Dr. Sheridan, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Noman Naseer Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0319760.r005

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