PONE-D-23-37915Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.PLOS ONE

Dear Dr. Tørring,

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Additional Editor Comments:

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The reviewers have praised the work for significant contributions on human activity recognition in typically developing children and those with Cerebral Palsy but suggests improvement in addressing methodological concerns. Specific suggestions include clarifying data acquisition methods, questioning biases in training models (by both reviewer 1 and 3), and addressing discrepancies in group sizes. The reviewers 2 and 3 have common concerns about tangential literature and references used. The reviewer 1 have marked suggestions over the manuscript's pdf (see attachment), helping authors to address minor technical and langauge concerns. Finally, per the reviewer 3 suggestions, the authors are urged to discuss the implications of group sizes and consider ways to enlarge the free play dataset.

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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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #3: 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

Reviewer #3: 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: typically developed children. Overall, on the technical side, the presented manuscript is technically sound, and all the result data support the authors' claims in the result and conclusion sections. The authors' choice of F-1, understanding of F-1, and usage are respectable. Under the microscope, however, there are minor editorial/technical errors—for example, the authors' inconsistent citation style or difficulty in explaining their 12 models concept. Furthermore, the authors haven't made any data available for public consumption. Please refer to the reviewer attachment for a complete list of questions/comments in the form of notes embedded in the manuscript PDF. Overall, the reviewer concluded that this manuscript is legible and technically sound, and all the analyses have been done appropriately.

Reviewer #2: Authors have presented an ML-based Human Activity Recognition (HAR) model for typically developing (TD) children and children with Cerebral Palsy (CP).

The study discusses the advantages of HAR models for the same and challenges in the setup and hyperparameters such as training data, optimal accelerometer placements,

and window size considerations. It presents the usage of the XGBoost classifier (12 sub-models) with 1, 3, and 5 sec time windows. The ML-based approach [1, 2, 3] is a very common HAR.

Following are my comments to further improve this article:

1. The applicability of the study may be broadened by including the usage of HAR in different health and rehabilitation fields such as exercise/YOGA pose classification [4], rehabilitation of individuals after sports injury, and improving the posture for the different exercises and training.

2. The study can also include a) a comparison of deep learning-based models [3, 4, 6] along with ML-based models for the HAR model for TD and CP children and b) a study of multimodal features [6] in HAR. Authors may refer to the studies [1, 5, 6] for choosing appropriate ML and DL methods for the task.

3. The author should consider the experimentation of multiple related datasets such as WISDM Activity Prediction, UCI HAR, DSADS, etc. Please refer to Table 1 from [6] for a list of publicly available datasets of HAR.

4. The possible direction to measure the automatic improvement in CP children after the rehab/therapy would further boost the applicability of research in a real-world application.

5. In line 73 of the Introduction section, TD is mistyped as TP.

6. In line 243 of section 2.6, it is written as F-measure is the harmonic mean between precision and sensitivity, where as it is the harmonic mean between precision and recall [7].

References:

1. Gupta, N., Gupta, S.K., Pathak, R.K. et al. Human activity recognition in artificial intelligence framework: a narrative review. Artif Intell Rev 55, 4755–4808 (2022). https://doi.org/10.1007/s10462-021-10116-x

2. Csizmadia, G., Liszkai-Peres, K., Ferdinandy, B. et al. Human activity recognition of children with wearable devices using LightGBM machine learning. Sci Rep 12, 5472 (2022). https://doi.org/10.1038/s41598-022-09521-1

3. D. Sakkos, K. D. Mccay, C. Marcroft, N. D. Embleton, S. Chattopadhyay and E. S. L. Ho, "Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy," in IEEE Access, vol. 9, pp. 94281-94292, 2021, doi: 10.1109/ACCESS.2021.3093469.

4. Vallabhaneni, N., Prabhavathy, P. Segmentation quality assessment network-based object detection and optimized CNN with transfer learning for yoga pose classification for health care. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08863-w

5. Wan, S., Qi, L., Xu, X. et al. Deep Learning Models for Real-time Human Activity Recognition with Smartphones. Mobile Netw Appl 25, 743–755 (2020). https://doi.org/10.1007/s11036-019-01445-x

6. Kaixuan Chen, Dalin Zhang, Lina Yao, Bin Guo, Zhiwen Yu, and Yunhao Liu. 2021. Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities. ACM Comput. Surv. 54, 4, Article 77 (May 2022), 40 pages. https://doi.org/10.1145/3447744

7. Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39, pp. 234-265). Cambridge: Cambridge University Press.

Reviewer #3: The article “Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy” is well written and pleasurable to read. It presents a classifying method of physical activities in CP children and above all the influence of different parameters of this method: learning dataset composed of only TD children or a mix of TD and CP children, the length of the observation window, the choice to use or not an overlap to define this window. Results are clear and complete which helps to understand the effect of each parameter. Collecting and processing the dataset has been a very consequent job that might be very useful for any research team working on physical activity recognition in CP children in real life which is a crucial question. I would like to sincerely thank the authors for that huge and useful work.

Nevertheless, some methodological choices and the ensuing elements of discussion should be address to strengthen the impact of this study.

Specific comments

L44: As soon as the abstract you should indicate how data are acquired (video, IMU, … ?)

L85: It is a bit surprising that in most of previous studies, the authors did not think that training the model with one population (adults) would be a bias when using the model with another one (children). This bias is presented as on major argument for your study. However, I am pretty sure this potential bias is largely exaggerated as in most studies aiming at recognize human activities the training population has the same characteristics as the target one.

L99: And even, the three articles you present come from the same research team in Queensland, Australia. I am very surprised that only two research teams in the world are interested in PA recognition in CP. Are you only focusing on the articles in which the methodology used for this recognition is very close to yours so that comparison is easier? If so that needs to be modified. You should be able to compare your results even when the classifier’s principle is different from yours.

L115: It is not clear what are the ‘two’ models. There is no prior justification for the need of two different models and what will be the advantage of one vs. the other, or in which cases one should be used. Just after, I understand that you mean two models because learning data are not the same, e.g. one with only TD and the other one mixing TD and CP. The question is to know if is the methodology identical in both cases. If it is, I would rather say that there is actually only one model with which you are testing the influence of identifying the model’s parameters from two learning datasets. Indeed, afterwards you are talking about 12 models when combining this learning dataset parameter to two other ones, i.e. length of window (1, 3 or 5 s) and the overlap.

L123: The two groups have very different size (ratio 1:4). It means that in your second dataset, mixing TD and CP, TD activities are actually the large majority. On L201, you don not indicate if the second dataset includes all data from TD children. I thus assume that it is the case. It actually means that your second ‘model’ center of gravity is actually drifted towards TD movements.

L155: What is the sensitivity of your method to accelerometer misplacement? The final goal is to monitor activities in real life situation in which CP children or their parents will place the devices. They are not clinicians trained to the use of such device. One usual error in that case is axes misalignment. It is important to test this robustness to that kind of bias. In your experiment, these biases were absent as all the devices were placed by the team (maybe even the same operator). It implies that the data used in the LOSOCV evaluation is homogeneous regarding these biases. As being outside the learning database, the child left-out represents a subject in real life. But actually, sensors’ placement on a child in real life will be less precise.

L197: Fig. 1 is hardly readable.

Discussion: you should discuss a bit more about the groups’ sizes. I agree with you that your whole dataset with 79 children is quite large compared to other studies. But 80% of them are TD. And moreover, as some activities (e.g. jumping) is more difficult for CP children, when focusing more finely on the dataset activity per activity, this ratio is sometimes even more in favor of TD children.

L462: I agree with you that it is always a bit frustrating not to have more free play datasets. Free play, even in clinical environment, is one important way to be closer to real life. You honestly recognize that your free play dataset should be enlarged but not indicating how this could be done. If you have an idea, could you explain it briefly?

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Reviewer #1: No

Reviewer #2: Yes: Dr. Durgesh Kumar

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PONE-D-23-37915_with-comments.pdf

PONE-D-23-37915R1Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.PLOS ONE

Dear Dr. Tørring,

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.

Please submit your revised manuscript by May 16 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jyotindra Narayan

Academic Editor

PLOS ONE

Additional Editor Comments:

==============================

Reviewer #1 recommends accepting the manuscript, acknowledging its contribution to Human Activity Recognition (HAR) in typically developing children and children with cerebral palsy. While noting improvements in presentation and writing, Reviewer #2 raises concerns about the lack of novelty in methodology and research objectives. Suggestions for enhancement include incorporating a deep learning-based model for comparison with XGBoost, utilizing multimodal features combining video image and sensor data, sharing data with the research community, and addressing how the proposed models differ from existing methods. Reviewer #3 appreciates the authors' responses to concerns but suggests including unpublished results regarding sensor misplacement's impact on model performance as a potential limitation.

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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: (No Response)

Reviewer #3: (No Response)

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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: Partly

Reviewer #3: (No Response)

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

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: No

Reviewer #3: (No Response)

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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

Reviewer #3: (No Response)

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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: (No Response)

Reviewer #2: The manuscript solves a real-world problem of Human Activity Recognition (HAR) in normal children and children with celebration palsy. The authors have improved in presentation and writing from the first revised version. However, the manuscript lacks novelty in terms of the proposed methodology and research Objectives. The significant research contributions of the study are as follows:

1. The novel datasets for Human Activity Recognition for Typically Developing Children (TD) and Children with Cerebral Activity.

2. Experiment and Analysis of HAR on the above datasets using XGBoost classifier.

3. Study of the impact of windows size (1 sec, 3 sec, 5 sec) and with overlapping.

In my first review, I have provided possible direction to extend the novelty and technical contribution of the paper by including:

1. Inclusion of a Deep learning-based model (such LSTM) for comparison with XGBoost: The author refused to experiment with the model, citing their assumption that it would not improve their results further without even experimenting with it. LSTM and BiLSTM are known to improve the results in sequential data. Further, the authors claimed there was a lack of space to fit their results, as they had too many large result tables. The results table could appropriately be compressed by representing it with suitable diagrams.

2. Inclusion of multimodal features: I mean to use both the video image and sensor data for the HAR. As the confusion matrix (Figure 5) NNTU HAR ChildrenCP-5 sec shows, some of the Walking and running data is wrongly predicted as Standing. In such a scenario, the image taken from the camera could assist the sensor data in proper activity recognition.

3. The authors have highlighted the lack of publicly available data for HAR in children and children with CP. Unless researchers share their data for academic and research purposes, this problem will remain as it is. Therefore, it is requested that the authors share their data with the research community after the journal accepts it. Please share the data sharing policy and some data samples with the Editorial Team before the publication of the papers.

4. How much do the Model NNTU-HAR-Children and NNTU-HAR-ChildrenCP differ from the NNTU-HAR method proposed by Roar M. Fenne?

Other Minor comments:

1. In section 2.4.2 (Video Annotation), it is not clear how many annotators were there in total, and each video frame annotated by how many annotators, and what was the score inter-annotator agreement?

2. The research has not concluded the optimal time window for the HAR in their conclusion section and results discussion section.

Reviewer #3: The authors provided convincing answers to my concerns and consequently modified the article. There is only one concern for which extra information would be useful. Regarding robustness to sensors' misplacement, the authors explained that "Our research group have tested the consequence of sensor misplacement on model performance in previous studies using

the same methodology (unpublished). As long as the sensors are placed in the correct direction towards gravity minor deviations in placements does not affect the accuracy of the model." Even if unpublished yet, the results of these previous studies could be presented in discussion as a potential limitation already taken into account. In the current version, it is as if the limitation does not exist.

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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: Dr. Durgesh Kumar

Reviewer #3: No

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[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. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-23-37915R2Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.PLOS ONE

Dear Dr. Tørring,

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.

Despite the reviewers agreed about the significant revisions made by the authors, they are still not convinced on the novelty and technical contribution of the work. The authors should present the model file and include some results for deep learning-based experiments in the manuscript to improve the technical contributions of the work adequately. 

Please submit your revised manuscript by Aug 08 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jyotindra Narayan

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

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 #2: All comments have been addressed

Reviewer #3: 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 #2: Partly

Reviewer #3: (No Response)

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

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 #2: No

Reviewer #3: (No Response)

**********

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 #2: Yes

Reviewer #3: (No Response)

**********

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 #2: The manuscript lacks significant technical contribution except for noble datasets. However, there had been few publicly available HAR datasets ( but not with children and cerebral palsy). Several works have already been on HAR for different age groups and people with cerebral palsy. However, as per the author's claim, this is the first work on HAR in children with Cerebral Palsy.

The GitHub repository https://github.com/ntnu-ai-lab/harth-ml-experiments, provided by the user, contains well-documented code, except the saved model files. The author should publish the model file in the GitHub repo for the reproducibility of the result. The GitHub repository already contains the code for BILSTM and CNN on the same datasets. Therefore, the authors are requested to include the results of the deep-learning-based experiments (biLSTM, CNN) in the current manuscript to justify the technical contribution and the PLOS ONE journal's reputation. There are already a few existing resources (research papers, HAR datasets, GitHub public repository containing ML, Deep learning and other methods for HAR). There has been no significant improvement in technical contribution from the first submission except for the improvement in writing and clarity of the manuscript.

Some references (published articles, HAR datasets, public GitHub repositories) are provided below:

Existing related papers on the HAR:

1. D. Ravi, C. Wong, B. Lo and G. -Z. Yang, "Deep learning for human activity recognition: A resource efficient implementation on low-power devices," 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA, 2016, pp. 71-76, doi: 10.1109/BSN.2016.7516235. keywords: {Machine learning;Feature extraction;Spectrogram;Convolution;Time-frequency analysis;Data mining;Deep Learning;Low-Power Devices;HAR;ActiveMiles},

2. M. Mostafavizadeh, A. R. Sadri and M. Zekri, "Walking pattern classification in children with cerebral palsy: A wavelet network approach," The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), Shiraz, Iran, 2012, pp. 243-249, doi: 10.1109/AISP.2012.6313752. keywords: {Classification algorithms;Acceleration;Force;Legged locomotion;Entropy;Feature extraction;Accelerometers;Cerebral Palsy;kinetic data;Accelerometer;Pattern Classification;Wavelet Network;Shannon entropy},

3. Csizmadia, G., Liszkai-Peres, K., Ferdinandy, B. et al. Human activity recognition of children with wearable devices using LightGBM machine learning. Sci Rep 12, 5472 (2022). https://doi.org/10.1038/s41598-022-09521-1

4. Taborri, J.; Scalona, E.; Palermo, E.; Rossi, S.; Cappa, P. Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy. Sensors 2015, 15, 24514-24529. https://doi.org/10.3390/s150924514

5. J. Kamruzzaman and R. K. Begg, "Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait," in IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2479-2490, Dec. 2006, doi: 10.1109/TBME.2006.883697.

6. Pengxi Fu, Jianxin Guo, Hongxiang Luo, LightGBM for Human Activity Recognition Using Wearable Sensors. Automation and Machine Learning (2024) Vol. 5: 113-118. DOI: http://dx.doi.org/10.23977/autml.2024.050114

7. Malekzadeh, M., Clegg, R., Cavallaro, A., & Haddadi, H. (2021). Dana: Dimension-adaptive neural architecture for multivariate sensor data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(3), 1-27.

8. Phyo, C. N., Zin, T. T., & Tin, P. (2019). Deep learning for recognizing human activities using motions of skeletal joints. IEEE Transactions on Consumer Electronics, 65(2), 243-252.

HAR datasets:

1. UCI-HAR : https://archive.ics.uci.edu/dataset/344/heterogeneity+activity+recognition - containing 30 users performing 6 activities. Accelerometer and gyroscope data were collected by a smartphone worn on the waist

2. UTwente: https://www.mdpi.com/1424-8220/16/4/426 includes data of 10 users performing 13 activities using accelerometer, gyroscope, and magnetometer data collected from the device on the (right) wrist.

3. MobiAct: https://www.scitepress.org/papers/2016/57924/57924.pdf

4. MotionSense: https://arxiv.org/abs/1802.07802

Public Github repository on HAR:

1. https://github.com/mmalekzadeh/motion-sense/tree/master

2. https://github.com/mmalekzadeh/dana

3. https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition

4. https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN

Reviewer #3: (No Response)

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Reviewer #2: Yes: Dr. Durgesh Kumar

Reviewer #3: Yes: Armel Crétual

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Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.

PONE-D-23-37915R3

Dear Dr. Tørring,

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Additional Editor Comments (optional):

Following the successive revisions and authors' reponse to the reviewer concerns, the mansucript is now recommened for acceptance and publication. Congratulations to the authors fo the good work.

Reviewers' comments: