Clinical Features to Predict the Use of a sEMG Wearable Device (REMO^®) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

Round 1

Reviewer 1 Report

This work aims to define the clinical features of stroke survivors using Remo® in three conditions: 1, 5, or 10 hand gestures control. The paper's contribution to existing knowledge in this research field is not well justified. The paper needs to contribute more, and the following points can improve the manuscript.

 

1.     The title can be improved.

2.     Enhance the abstract and introduction to show the motivation for this work.

3.     A comparative study can be added to the introduction or a related work section in table form to show the recent efforts.

4.     Figure 3 should be improved.

5.     Figure 4 caption can be shifted to the test.

6.     Discuss the classification method mathematically.

7.     Add a list of abbreviations.

8.     The proposed method should be compared with more recent techniques.

9.     References should be updated; there are no references in 2021 or 2022.

10.  The manuscript organization should be improved. 

11.  Improve the English of the work. There are too many problems with paper typesetting.

12.  Change the “Conclusion” section title to “conclusion and future directions” and add more discussion and future directions to the research.

 

13.  The paper is unsuitable for acceptance in its current form. The article needs rewriting to address the comments mentioned above. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper aims to find a relationship between stroke survivors' characteristics and a computer interface's (called Remo©) ability to recognize ten hand gestures based on low-density sEMG signals recorded at the forearm level. The authors found that residual motor function, pain, joint restriction, and spasticity at the upper limb predict the ability of their computer interface to recognize hand gestures. The authors' results help define which stroke survivors could be referred to treatment with the Remo© computer interface.

The Methods are less developed than I would expect to see in an academic journal article.

• The authors should describe how the maximum voluntary contraction (MVC) was registered to define the contraction Ratio (CR).

• Authors also must state the model and formula used to calculate the sample size.

• Most of the analysis performed by the authors was based on the stratification of the identification of hand gestures by the Remo© computer interface. The authors proposed three a priori strata (i) zero identified movements, ii) five identified movements, and iii) ten identified movements) followed by a fourth stratum defined using supervised K-means cluster analysis (iv) six identified hand gestures). Although this analysis has academic merit, I found it difficult to follow the authors' initial argument and supposed validation. This analysis would benefit from restructuring to simplify this section, considering the clinical & research significance and interest for the reader, as the authors did in the discussion section. For example, from the beginning, stratum iii can be substituted by stratum iv.

• It is recommended that the authors include any form of validation of their logistic multivariable regression (GLM) models and cluster analysis, such as cross-validation. Also, a more detailed description of GLM and cluster analysis could be of benefit, for example, stating explicitly all the parameters used for each analysis.

• Also, I found it difficult to follow the author's argument about the safety and feasibility in the introduction and results of the device since no related methodology is described.

• I also found it difficult to follow the author's argument about the easiness of classification of some hand gestures and their association of them with muscle activation patterns (lines 313-325) since their differences were found in less than 10% of the subjects and the authors did not report any results about the association of hand gestures with specific muscle activation patterns.

• Finally, more discussion is needed to compare the authors' results to those of other authors.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

The aim of this study is to define the gesture control features of patients that have survived stroke using a device REMO  in three conditions of gesture control.  The results are encouraging.  The concept of the work has been explained well however the language needs improvement throughout the manuscript.

Minor comments:

1.      There are plenty of grammar mistakes here and there, For, example line 126, page 3, replace “is composed by” to “is composed of”

2.      Line 187, was there any time interval between the performance of the 10 gestures? As it is mentioned that each pattern was recorded for 3 seconds. It will be good if the pictorial representation of these ten signals will be given to differentiate between different gestures and their signals.

3.      How many times each participant was asked to repeat a gesture? Probably 2 times. Was there any participant who could not make any specific gesture? Or were some gestures considered easy/difficult to make.

4.      Similarly were some gestures easily detected as compared to others?

5.      Line 197, what is meant by “purpose them”, kindly rephrase it.

6.      Line 200-201 rephrase

7.      Line 212, what is meant by divers pattern?

8.      Line 237, concluded or completed?

9.      Only 6 out of the 44 references are from the past 5 years. It is recommended to add related literature, similar methods, and devices to have a good overview of the method developed.

10.   Authors should also discuss the advantages of this method over other state-of-the-art methods of gesture control available in the field.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have addressed most of my concerns. The paper can be accepted.