PONE-D-24-14729Estimating intra-subject and inter-subject oxygen consumption in outdoor human gait using multiple neural network approachesPLOS ONE

Dear Dr.  Müller,

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PLOS ONE

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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: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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: Yes

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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

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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 authors have studied the problem of estimating oxygen consumption in outdoor human gait using different deep neural networks. The problem studied is interesting, and can be helpful in analyzing organs in physical activities.

Generally, I would say that the contribution is good. However, it lacks a good presentation. It seems that the paper has been written very quickly, lacking enough attention to details. Thus, I recommend a careful rewrite, especially in the section of networks’ architectures. I also suggest the following modifications:

1- Title of the paper: the phrase “using multiple neural network approaches” sounds strange. It is better to replace it with “using deep neural networks”

2- When abbreviating a word, it is better to start the word you would like to abbreviate with a capital letter, and then put the abbreviation in parentheses. For example: Hearth Rate (HR)

3- When introducing a new concept for the first time, then it is emphasized (italic) and later use of this concept shouldn’t be italic. You have made “LSTM” italic in several places between the lines 168-197. Please correct it

4- The reference in Line 181 is missed

5- Please arrange the features of the test subjects, described in Lines 218-213, in a well-organized table.

6- Line 247 has an open parenthesis without closing

7- Please re-illustrate all figures in the paper and improve their quality

8- “Xception” should always be used with a capital “X”. Please correct it in all places

9- Please recheck the usages of emphasized (italic) words in the whole text

10- Please make the implementations available in a public repository to check validity of the results

Reviewer #2: I read with eager interest the paper “Estimating intra-subject and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.” My impression of the manuscript is extremely positive.

This is a well-conceived, well-executed, and well-written study. However, I believe the manuscript could be further improved to enhance the clarity of some messages, terminology, and structure. I hope the authors will consider the following comments as constructive feedback intended to benefit both the authors and readers.

In some cases, the authors could better highlight the positive contributions of this manuscript to the literature. In other cases, certain passages need to be clarified for the readers. Additionally, readers might appreciate it if the conclusion is framed to be relevant to the readership of PLOS ONE, as it is currently quite technical.

Strengths of the manuscript include providing valuable insights into the difference between inter and intra-subject estimation, making the dataset fully available, building on previous open-access work, and conducting the study outdoors rather than on treadmills, which is a noteworthy point to highlight. The authors also address the limitation regarding the presence of ventilatory thresholds, which can significantly affect VO2 predictions.

The authors state in the abstract that “[This technology] could be embedded in portable devices for real-time estimation of oxygen consumption during walking and running.” However, the authors did not assess the capability of this system as a real-time model, making this statement misleading. The authors did not report computational time or the energy resources needed to run the neural network on a portable device. Implementing these neural networks in production involves several challenges beyond input feature measurement, including anthropometric data reception. Therefore, it is premature to conclude that this neural network can be embedded in portable devices, as this work primarily focuses on accuracy assessment, not the performance of the models on real-time embedded devices.

On this note, a limitation of the methodology that was not discussed is that it may not respond promptly to changes in gait speed due to the use of long data windows and the rolling average of VO2. In fact, no time series are reported in the manuscript, which again makes it difficult to assess the potential of this application to work on a real-time basis. Bland-Altman is gold-standard (ish) to evaluate accuracy of measurements, but makes it difficult for the reader to understand how the system behaves when walking speed or running velocity changes in real-time. Again, I do not think authors should place too much emphasis on the real-time application, since time-series were not evaluated and errors in the estimation are not plotted or discussed on the time basis, and the experimental settings have not been designed to assess the performance of the system during transitions and highly dynamic settings. Authors might want to add to the discussion about these points.

The implications for the use of this technology in outdoor and natural settings should be clarified. Can the technology reliably monitor physical activity? What are the main application settings, and can the authors contextualize the results within these applications? Is the accuracy sufficient for the intended applications?

In comparison with the current literature, this manuscript offers an important innovation: the use of multi-head structures in predicting VO2 at the inter-subject level, which is a valuable addition. I congratulate the authors for this brilliant solution. However, it is crucial to clarify how the dataset was used and ensure that data from one subject did not contaminate the dataset used to train/test the neural networks for other subjects. For example, the authors should explicitly detail the normalization process, clearly stating that some subjects were excluded from any part of the training process, including feature normalization. For example: at L164-166, please clarify the type of normalization used (e.g., robust, min-max, or standardization) and whether intra or inter-subject data were used for normalization.

Assessing the correlation coefficient for each variable separately can lead to misleading conclusions due to potential cross-correlation between variables and non-normally distributed data. Alternative methodologies for studying feature importance should be considered. The choice of statistical approach with deep learning models should be justified.

Please clearly specify the dimension of the input tensor (e.g., [5x50]?) at L161. This information, though mentioned later, would be beneficial at this stage.

In general, to avoid confusion, it is advisable to separate the Results and Discussion sections.

In the abstract, it is suggested to define XceptionNet or use the structure/type of neural network and report numerical results if possible.

Throughout the manuscript, there seems to be confusion between the terms "parameters" and "variables," and then input features. In modeling, parameters are constant values, while variables describe the system's evolution. Features are those used as input into deep learning and machine learning models. This inconsistency appears in the abstract and throughout the manuscript, including the methods section where the term "breathing variables" is used. It is important to maintain consistent terminology.

Minor comments include:

Consider a slight modification to the title for brevity: “Estimating intra and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.”

At L176, please indicate if the test accuracy/loss was checked to determine the number of epochs.

A citation is missing at L181.

A parenthesis appears to be missing at L247.

Please increase the font size in Figure 8.

Remove the word "only" at L355, as it is arbitrary and this should be a Result section.

There is a typo at L362: “wre” should be corrected.

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Reviewer #1: No

Reviewer #2: Yes: Andrea Zignoli

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Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches

PONE-D-24-14729R1

Dear Dr. Philipp Muller,

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.

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Kind regards,

Aamna AlShehhi, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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 #2: All comments have been addressed

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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 #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: 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 #2: Yes

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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 #2: 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: Thanks for addressing all the comments. The revised version of the paper perfectly fits to be published in the PLOSE ONE journal.

Reviewer #2: Thank you to the authors for thoroughly addressing all the comments and for taking the time to revise the manuscript according to the reviewers' recommendations. This work is a valuable contribution to the current literature on the topic.

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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 #2: Yes: Andrea Zignoli

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