Design of a Smart Foot–Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper introduces an intelligent ankle-foot orthosis integrated with multiple sensors, which is used to monitor the gait parameters of patients with foot drop in real time and transmit data via Bluetooth to enable remote rehabilitation assessment.The research and experiments conducted by the author are relatively detailed, but I would like to put forward the following suggestions here.

1. The paper mentions that data is transmitted to smartphones via Bluetooth, but it does not specify how patients or doctors interpret this data. Are there supporting apps or visualization tools?
2. The experimental section is mainly based on short-term tests and does not involve the reliability of long-term use (such as sensor drift and material wear).
3. There are some related works on Sensor Technology, health monitoringare suggested to cite, such as:

DOI：10.1007/s11432-023-3801-1

DOI10.1016/j.heliyon.2024.e30845

DOI10.1021/acsami.1c02815

https://doi.org/10.1016/j.cej.2025.163932

Author Response

This paper introduces an intelligent ankle-foot orthosis integrated with multiple sensors, which is used to monitor the gait parameters of patients with foot drop in real time and transmit data via Bluetooth to enable remote rehabilitation assessment. The research and experiments conducted by the author are relatively detailed, but I would like to put forward the following suggestions here.

1. The paper mentions that data is transmitted to smartphones via Bluetooth, but it does not specify how patients or doctors interpret this data. Are there supporting apps or visualization tools?

Answer: We appreciate the reviewer’s comment regarding data interpretation and system integration. The current research focuses on establishing the fundamental capability of a comfortable yet efficient system for monitoring foot drop. To demonstrate functionality, we developed an in-house experimental setup with Bluetooth-based data transmission. While various smartphone applications exist for gait monitoring, they require customization for the specific needs of our system. Professor Joe Priest, a co-author and expert in kinesiology, has interpreted the collected data to visualize different types of foot drop. We acknowledge that further detailed analysis and development are required, and in future work, we plan to create a dedicated smartphone application and design interpretation algorithms in collaboration with kinesiology experts to ensure clinically relevant assessments.

2. The experimental section is mainly based on short-term tests and does not involve the reliability of long-term use (such as sensor drift and material wear).

Answer:

3. There are some related works on Sensor Technology, health monitoring, which are suggested to cite, such as

Answer: The authors appreciate the reviewer’s suggestion. The manuscript has been thoroughly revised and rewritten, and the number of references has increased from 18 to 27, incorporating numerous recent publications.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Introduction

Is this "foot drop" or "footdrop"? There should be a consistency throughout the text.

Pg. 2, Line 79 - Was the system tested with two patients? If yes, it is not a clinical trial. You should change the previous statement. 

Materials and Methods

Pg. 5, Line 184 - This is equation 2, not equation 1. Equations should be cited in the text correctly.

Experimental Works

Figures are so blurry, they should be in better quality.

There is no explanation about the experimental protocol. What is the details of walking assessment? Waht is the meaning of "normal walking"? What is the speed of walking? What is the duration? Is there only one speed or did you assess different speeds? The figures show a treadmill, did you use the treadmill or the patients walked on a surface? These details have to be added. 

Pg. 10, Line 295 - "hell" - typo.

The authors claimed that the insoles in current research has inaccurate results but they do not have ay proof that their system showed accurate results. There is no comparison with validated measurement systems to prove the accuracy. 

There is no discussion! The results has to be discussed in a Discussion section by comparing with the state-of-the-art. It is a must to prove the novelty and innovation of the study. 

17 references is too low for this kind of study. There are much more studies which work on gait monitoring. The authors should to a more detailed literature review. 

Author Response

Reviewer 2:

Introduction

2.1 Is this "foot drop" or "footdrop"? There should be consistency throughout the text.

Answer: appreciate it, foot drop is correct. The text is revised to ensure it is consistent.

2.2 Pg. 2, Line 79 - Was the system tested with two patients? If yes, it is not a clinical trial. You should change the previous statement. 

Answer: Yes, it is two patients. We changed the statement in page 1, line 80 to “The smart insole has been evaluated through simulations and proof-of-concept testing on human subjects”.

2.3 Materials and Methods

Pg. 5, Line 184 - This is equation 2, not equation 1. Equations should be cited in the text correctly.

Answer: It is corrected to Eq. (2)

2.4 Experimental Works

Figures are so blurry, they should be in better quality.

There is no explanation about the experimental protocol. What are the details of the walking assessment? What is the meaning of "normal walking"? What is the speed of walking? What is the duration? Is there only one speed, or did you assess different speeds? The figures show a treadmill. Did you use the treadmil,l or did the patients walk on a surface? These details have to be added. 

Answer:

In this study, the primary goal was to demonstrate the feasibility of using a simple wearable system (Arduino with accelerometers and gyroscopes, and FBG sensors) to detect foot drop and measure foot sole deformation. The experimental protocol was designed as a controlled proof-of-concept evaluation, rather than a full clinical gait assessment. The details are as follows:

Walking Surface: Participants walked on a treadmill at a comfortable self-selected speed to standardize measurements and minimize variability in gait cycles. No overground walking was performed in this preliminary study.

Definition of “Normal Walking”: For comparison purposes, “normal walking” refers to a participant without foot drop performing a comfortable, self-paced treadmill gait. The speed was selected individually by each participant to reflect their natural gait.

Walking Speed and Duration: Walking speeds were set at approximately 1.0–1.2 m/s, representing a typical comfortable pace for adults. Each trial lasted approximately 2–3 minutes, allowing multiple gait cycles to be recorded. Only one speed per participant was used in this proof-of-concept study.

Assessment Purpose: The focus was on detecting relative differences in toe acceleration, ankle rotation, and sole deformation between a participant with foot drop and a participant without foot drop. Absolute clinical performance or long-term walking patterns were not assessed in this preliminary work.

Sensor Setup: Participants wore 3D-printed insoles embedded with accelerometers, gyroscopes, and FBG sensors. Data was recorded in real-time and transmitted via Bluetooth to the processing platform for immediate visualization.

This protocol allows for controlled and reproducible measurements while demonstrating that the proposed system can detect foot drop-related gait deviations and measure locomotion parameters in real time. A more detailed clinical assessment, including multiple speeds, overground walking, and larger cohorts, will be addressed in future studies

2.5 Pg. 10, Line 295 - "hell" - typo.

Answer: The entire manuscript has been rewritten to correct typo/grammatical errors, including heel.

2.6 The authors claimed that the insoles in current research have inaccurate results, but they do not have any proof that their system showed accurate results. There is no comparison with validated measurement systems to prove the accuracy. 

Answer: We appreciate the reviewer’s comment regarding accuracy validation. The primary goal of this study was to demonstrate the feasibility of a simple, low-cost wearable system for detecting foot drop and monitoring foot deformation, rather than to perform a full clinical validation.

Proof-of-concept validation: The system was tested using a 3D-printed insole prototype with multiple sensors (accelerometers, gyroscopes, FBG sensors) during controlled treadmill walking. Parameters such as toe acceleration, ankle rotation, and sole deformation were successfully measured in real time, showing clear differences between normal walking and foot drop patterns.

Focus on relative measurements: The study emphasizes relative detection of gait deviations rather than absolute clinical accuracy. Differences in key gait parameters between participants with and without foot drop were consistently observed, demonstrating the device’s potential for monitoring foot drop.

Future work for formal validation: We acknowledge that a formal accuracy comparison with validated gait analysis systems (e.g., motion capture systems, instrumented treadmills, or commercial smart insoles) is necessary. This will be addressed in future studies, which will include calibration, repeated trials, and statistical validation against standard reference systems.

Thus, while absolute accuracy against gold-standard systems has not yet been established, the proof-of-concept results confirm the feasibility and reliability of the system for detecting relative gait deviations caused by foot drop.

2.7 There is no discussion! The results has to be discussed in a Discussion section by comparing with the state-of-the-art. It is a must to prove the novelty and innovation of the study. 

Answer:

We have completely revised and rewritten the discussion section and highlighted yellow in the revised manuscript.

17 references are too low for this kind of study. There are many more studies that work on gait monitoring. The authors should do a more detailed literature review. 

Answer:

The references have been updated to include the most recent publications in this field, with a total of 27 citations, most of which were published within the past five years.

 

Reviewer 3 Report

Comments and Suggestions for Authors

1. Title and Abstract

Major Concerns:

• The title is too long and could be more concise. Consider: “A Smart Foot-Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring”.
• The abstract lacks numerical or quantitative results. Please include at least one key experimental finding or performance metric.
• Terms like "off-axis displacement" and "malalignment" need clearer definitions—avoid vague technical jargon unless clearly explained.

 

• Include specific outcomes in the abstract (e.g., detection accuracy, response time).
• Remove repetitive statements; streamline the message.

 

2. Introduction

Major Concerns:

• The introduction includes general background but fails to clearly state the problem with existing devices and how your design improves upon them.
• Several statements are vague or unsubstantiated, e.g., “many existing smart insoles suffer from some limitations…”—what exactly are they, and how does your design address them?
• References [1]–[3] are dated or not particularly strong. More recent and high-impact citations are needed.

 

• Focus the problem statement on the limitations of current foot-drop monitoring systems.
• Clearly identify the research gap your work fills.
• Improve citation quality (include recent work from 2020–2024, preferably from IEEE, Elsevier, or MDPI journals).

 

3. Materials and Methods

3.1 Fundamental Terminology

Minor Concerns:

• This section reads like a textbook. It is too detailed for a research article and distracts from the scientific contribution.
• Terms like “unnormal walking” are grammatically incorrect—use “abnormal walking”.

 

• Move most of this to supplementary materials or reduce it substantially.
• Maintain technical tone and use precise, grammatically correct terminology.

3.2 Simulation of Gait Cycle

Major Concerns:

• The gait cycle explanation is clear but not connected to your sensor system. How do these phases translate to sensor data?
• Figure 1 is basic and lacks citation or context.

 

• Explain how your sensors specifically track each phase of the gait cycle.
• Include time-series illustrations comparing healthy vs. foot drop gait patterns from your device.

3.3 Mathematical Modeling

Major Concerns:

• The finite element modeling is described at length, but its practical relevance is unclear.
• No simulation results from the model are presented.
• Equation (1) and related matrices are overly complex for the scope of this paper without validation.

 

• Either simplify the modeling section or provide matching simulation results that validate your approach.
• Consider moving detailed derivations to an appendix or supplementary section.

 

4. Sensor Technology and Implementation

Major Concerns:

• No sensor calibration procedures are described.
• Sensor placement rationale is unclear. Why were the heel and dorsum chosen?
• No mention of sampling rates, data resolution, power requirements, or wireless transmission stability.

 

• Include a table summarizing key sensor specs (e.g., range, accuracy, sampling rate).
• Explain how noise is filtered and what algorithm is used for signal interpretation.
• Describe the Bluetooth communication protocol and real-time data handling.

5. Experimental Setup and Validation

Major Concerns:

• The experimental section is qualitative and lacks statistical rigor.
• No information is provided on:
• Number of trials per patient
• Baseline comparison
• Reproducibility
• Variability between steps or sessions
• Testing was done on only two patients, with no demographic or medical background disclosed.

 

• Expand the clinical evaluation to include more subjects and healthy controls.
• Report:
• Mean ± standard deviation for measured variables
• Detection accuracy of gait abnormalities
• Comparison with gold-standard methods (e.g., motion capture or force plates)
• Provide IRB approval number and informed consent statement if human subjects were used

6. Results and Figures

Major Concerns:

• Figures are unlabeled, poorly described, and not always referenced in the text.
• No error bars or statistical analysis in plots.
• Figures 13–19 are essentially raw plots with minimal interpretation.

 

• Redraw figures with proper axis labels, units, and captions.
• Discuss what each figure shows in terms of clinical relevance.
• Add summary statistics for each test (e.g., peak strain, gait cycle timing, inversion angle).

 

7. Discussion

Major Concerns:

• The discussion section is weak and overly descriptive.
• No comparison is made to similar studies or commercial devices.
• The term “tele-rehabilitation” is mentioned but not supported with any details on software, patient interface, or remote monitoring.

 

• Discuss:
• Limitations of the current prototype
• Practical challenges in home deployment
• Possible improvements (e.g., AI for gait classification, mobile app integration)
• Include comparison with existing wearable gait devices (e.g., Moticon, GaitUp, or PIx4-based systems).

8. Conclusion

Major Concerns:

• The conclusion is vague and repeats the abstract.
• It includes unsupported claims such as “high level of accuracy”.

• Clearly state:
• What has been demonstrated
• What remains to be done
• Specific future work (e.g., real-world trials, energy optimization)

 

9. Language and Formatting

Major Concerns:

• The manuscript has numerous grammatical and syntactic errors, such as:
• “unnormal walking” → should be “abnormal walking”
• “hell” → should be “heel”
• Sentence fragments and run-ons throughout

• Have the manuscript professionally edited by a native English speaker or use an academic editing service.
• Adhere to MDPI’s formatting standards (e.g., citation style, figure/table layout).

 

10. Ethical and Reproducibility Considerations

Major Concerns:

• No mention of IRB approval, informed consent, or data/code availability.
• No statement on reproducibility or open-source access.

• Add an ethics statement (mandatory if human subjects are involved).
• Declare whether raw data or source code is available to readers.
• Consider publishing datasets/code in a public repository (e.g., GitHub, Zenodo).

 

Comments on the Quality of English Language

Major Concerns:

• The manuscript has numerous grammatical and syntactic errors, such as:

  • “unnormal walking” → should be “abnormal walking”

  • “hell” → should be “heel”

  • Sentence fragments and run-ons throughout

 

• Have the manuscript professionally edited by a native English speaker or use an academic editing service.

• Adhere to MDPI’s formatting standards (e.g., citation style, figure/table layout).

Author Response

Reviewer 3:

1. Title and Abstract

Major Concerns:

• The title is too long and could be more concise. Consider: “A Smart Foot-Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring”.

Answer: It is changed to “A Smart Foot-Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring.”

• The abstract lacks numerical or quantitative results. Please include at least one key experimental finding or performance metric.

Answer:  Two sentences are added to the abstract: “It is realized that, on average, it has been observed that toe acceleration in foot drop is approximately twice that of normal walking. Therefore, among various gait parameters, toe acceleration may serve as an effective metric for the detection of foot drop.’

• Terms like "off-axis displacement" and "malalignment" need clearer definitions—avoid vague technical jargon unless clearly explained.

 Answer: This work presents an innovative smart foot-ankle brace equipped with multiple sensors to monitor foot drop patterns during walking. The embedded sensors measure angular rotation, translational displacement, acceleration, and joint misalignment.

• Include specific outcomes in the abstract (e.g., detection accuracy, response time).

Answer: A proof-of-concept prototype, consisting of a 3D-printed insole, nano Arduino controller, integrated gyroscopes, accelerometers, and a Fiber Bragg Grating (FBG) sensor array, demonstrated real-time detection of foot deformation and gait imbalances. Experimental results and simulations confirmed that the device accurately quantifies key locomotion parameters, including angular rotation, acceleration, and structural displacement.

• Remove repetitive statements; streamline the message.

Answer: The abstract has been rewritten as” Foot drop, a form of paralysis affecting ankle and foot control, impairs walking and increases the risk of falls. Effective rehabilitation requires precise monitoring of gait to guide personalized interventions. This work presents a smart foot-ankle brace integrated with sensors measuring angular rotation, translational displacement, acceleration, and joint misalignment. The system analyzes these data to detect foot dragging, malalignment, and pressure distribution, transmitting results via Bluetooth to a processing platform. A proof-of-concept prototype, comprising a 3D-printed insole, nano Arduino controller, gyroscopes, accelerometers, and a Fiber Bragg Grating (FBG) sensor array, demonstrated real-time detection of foot deformation and gait imbalances. Experimental validation and simulations confirmed accurate quantification of locomotion parameters, with toe acceleration in foot drop observed to be approximately twice that of normal walking. Among the gait metrics, toe acceleration emerged as a reliable indicator for detecting foot drop. This smart brace provides clinicians with actionable insights into patient mobility, supporting data-driven diagnosis and personalized rehabilitation strategies.”

1. Introduction

Major Concerns:

• The introduction includes general background, but fails to clearly state the problem with existing devices and how your design improves upon them.
• Several statements are vague or unsubstantiated, e.g., “many existing smart insoles suffer from some limitations…”—what exactly are they, and how does your design address them?
• References [1]–[3] are dated or not particularly strong. More recent and high-impact citations are needed.
•  Focus the problem statement on the limitations of current foot-drop monitoring systems.

• Clearly identify the research gap your work fills.
• Improve citation quality (include recent work from 2020–2024, preferably from IEEE, Elsevier, or MDPI journals).

 Answer: The whole introduction, including the literature review, has been revised to include recent publications.

1. Materials and Methods

3.1 Fundamental Terminology

Minor Concerns:

• This section reads like a textbook. It is too detailed for a research article and distracts from the scientific contribution.
• Terms like “unusual walking” are grammatically incorrect—use “abnormal walking”.

 Move most of this to supplementary materials or reduce it substantially.

• Maintain technical tone and use precise, grammatically correct terminology.

Answer: the entire manuscript, including Section 3, 1, has been rewritten and highlighted in yellow.

3.2 Simulation of Gait Cycle

Major Concerns:

• The gait cycle explanation is clear, but not connected to your sensor system. How do these phases translate to sensor data?

Answer: This section outlines the normal gait cycle, where plantar flexion typically ranges from 0–55° and dorsiflexion from 0–25°. These ranges highlight the importance of incorporating gyroscopes to accurately capture angular variations during gait analysis.

 

Figure 1 is basic and lacks citation or context.

Answer: This figure was developed by Dr. Joe Priest, who is a professor of kinesiology and co-author of this paper.

•  Explain how your sensors specifically track each phase of the gait cycle.

• Include time-series illustrations comparing healthy vs. foot drop gait patterns from your device.

Answer: We believe including time-series plots in the Introduction would not be appropriate, as this section is intended to provide background and context rather than results. Time-series analyses are already presented in the Results section, where the x-axis represents time and the y-axis corresponds to measured parameters such as heel acceleration, toe acceleration, and angular rotation.   

3.3 Mathematical Modeling

Major Concerns:

• The finite element modeling is described at length, but its practical relevance is unclear.
• No simulation results from the model are presented.
• Equation (1) and related matrices are overly complex for the scope of this paper without validation.

• Either simplify the modeling section or provide matching simulation results that validate your approach.

• Consider moving detailed derivations to an appendix or supplementary section.

Answer:

This section aims to describe how strain measurement and acceleration measurement can indicate the motion of the foot. The details expression is already moved to the appendix.

 

1. Sensor Technology and Implementation

Major Concerns:

• No sensor calibration procedures are described.
• Sensor placement rationale is unclear. Why were the heel and dorsum chosen?
• No mention of sampling rates, data resolution, power requirements, or wireless transmission stability.
• Include a table summarizing key sensor specs (e.g., range, accuracy, sampling rate).

• Explain how noise is filtered and what algorithm is used for signal interpretation.
• Describe the Bluetooth communication protocol and real-time data handling.

Answer:

We acknowledge the reviewer’s concern regarding calibration. In this study, the primary objective was to compare relative differences between normal and abnormal gait patterns (specifically foot drop), rather than to obtain absolute kinematic measurements. For this reason, formal calibration was not required, as both healthy and foot drop data were collected using the same device under identical conditions, ensuring consistency across trials. Thus, relative variations between gait types remain valid without additional calibration. Nevertheless, we recognize that calibration is essential for clinical translation or absolute measurement studies, and we have noted this as a future consideration.

1. Experimental Setup and Validation

Major Concerns:

• The experimental section is qualitative and lacks statistical rigor.
• No information is provided on:
• Number of trials per patient
• Baseline comparison
• Reproducibility
• Variability between steps or sessions
• Testing was done on only two patients, with no demographic or medical background disclosed.

• Expand clinical evaluation to include more subjects and healthy controls.

• Report:
• Mean ± standard deviation for measured variables
• Detection accuracy of gait abnormalities
• Comparison with gold-standard methods (e.g., motion capture or force plates)
• Provide the IRB approval number and informed consent statement if human subjects were used

Answer:

We appreciate the reviewer’s detailed comments regarding calibration, sensor placement rationale, sampling rates, power requirements, and communication protocols. We would like to clarify that the current study is intended as a proof-of-concept experiment, rather than a clinical trial or finalized medical device evaluation. Therefore, our focus was on demonstrating the feasibility of detecting differences between normal and abnormal gait patterns (specifically foot drop) using integrated sensors, rather than on reporting exhaustive device specifications or clinically validated calibration procedures.

For consistency, all measurements (healthy and abnormal gait) were obtained using the same device and setup, which allows for valid relative comparisons without the need for calibration or clinical-grade validation at this stage. We agree that for future translation into clinical applications, detailed calibration protocols, hardware specifications, and communication performance analysis will be essential, and we have noted this explicitly as part of our planned future work.

 

1. Results and Figures

Major Concerns:

• Figures are unlabeled, poorly described, and not always referenced in the text.
• No error bars or statistical analysis in plots.
• Figures 13–19 are essentially raw plots with minimal interpretation.

• Redraw figures with proper axis labels, units, and captions.

• Discuss what each figure shows in terms of clinical relevance.
• Add summary statistics for each test (e.g., peak strain, gait cycle timing, inversion angle).

 Answer:

The entire manuscript is now modified. There are comprehensive and descriptive captions for each figure. All modifications are highlighted in yellow in the new manuscript. For the sake of brevity, the changes are not rewritten here. Just as an example on page 14, line 404, we have “The maximum toe acceleration measured along two directions was approximately 3.7 and 4.7 m/s², compared to 0.5–0.8 m/s² during normal walking. The system successfully captured these differences in gait parameters, demonstrating its potential to identify abnormal gait events in real time”.

1. Discussion

Major Concerns:

• The discussion section is weak and overly descriptive.
• No comparison is made to similar studies or commercial devices.
• The term “tele-rehabilitation” is mentioned but not supported with any details on software, patient interface, or remote monitoring.

 Discuss:

• Limitations of the current prototype
• Practical challenges in home deployment
• Possible improvements (e.g., AI for gait classification, mobile app integration)
• Include comparison with existing wearable gait devices (e.g., Moticon, GaitUp, or PIx4-based systems).

Answer:

We appreciate the reviewer’s suggestion to compare our prototype with existing commercial gait analysis systems. However, the primary objective of this work is not to benchmark against commercial platforms, but rather to demonstrate the feasibility of a simple, low-cost system that combines Arduino-based processing with inertial sensors and FBG technology for detecting foot drop. The novelty lies in showing how readily available components can provide reliable measurements of gait abnormalities and sole deformation, serving as a proof of concept for future development.

In the revised manuscript, we have clarified this scope and expanded the Discussion to highlight the limitations of the current prototype, the practical challenges in deployment, and directions for future improvements, including wireless integration, mobile applications, and AI-based gait analysis. This ensures that the Discussion remains focused on the research contribution while addressing the reviewer’s concern about depth and context.

1. Conclusion

Major Concerns:

• The conclusion is vague and repeats the abstract.
• It includes unsupported claims such as “high level of accuracy”.

• Clearly state:
• What has been demonstrated
• What remains to be done
• Specific future work (e.g., real-world trials, energy optimization)

1. Language and Formatting

Major Concerns:

• The manuscript has numerous grammatical and syntactic errors, such as:
• “unnormal walking” → should be “abnormal walking”
• “hell” → should be “heel”
• Sentence fragments and run-ons throughout

• Have the manuscript professionally edited by a native English speaker or use an academic editing service.
• Adhere to MDPI’s formatting standards (e.g., citation style, figure/table layout).

 Answer: The entire manuscript has been rewritten, and for the sake of brevity, the changes are not rewritten here; instead, they are highlighted in yellow.

 

1. Ethical and Reproducibility Considerations

Major Concerns:

• No mention of IRB approval, informed consent, or data/code availability.
• No statement on reproducibility or open-source access.

• Add an ethics statement (mandatory if human subjects are involved).
• Declare whether raw data or source code is available to readers.
• Consider publishing datasets/code in a public repository (e.g., GitHub, Zenodo).

 Answer:

Section 10: We have submitted the IRB along with the paper submission; it looks like the reviewer had no access to the submitted documents.

Ethics statement: All experimental procedures involving human participants were conducted in accordance with institutional guidelines and approved by the IRB, which was submitted along with the manuscript submission. Informed consent was obtained from all participants.
Data availability: The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions, but can be obtained from the corresponding author on reasonable requests.

 

Comments on the Quality of the English Language

Major Concerns:

• The manuscript has numerous grammatical and syntactic errors, such as:
• “unnormal walking” → should be “abnormal walking”
• “hell” → should be “heel”
• Sentence fragments and run-ons throughout

•  Have the manuscript professionally edited by a native English speaker or use an academic editing service.
• Adhere to MDPI’s formatting standards (e.g., citation style, figure/table layout).

 

 Answer:

This comment has already been addressed in the previous section. It is also implemented in the revised manuscript and highlighted in yellow.

 

 

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

despite improvements from the previous version, several critical issues remain unresolved.

1. Validation and Benchmarking

• The manuscript continues to lack validation against gold-standard gait analysis systems (e.g., motion capture, instrumented treadmill). While you acknowledge this as future work, at this stage of revision, some form of benchmarking or error quantification is expected.

• Without comparative data, the reliability and accuracy of the proposed device remain unclear.

2. Sample Size and Clinical Relevance

• Testing is limited to a very small number of participants, essentially case-based demonstrations.

• No statistical analysis or variability assessment is provided. For a journal article, this limitation severely reduces generalizability and the clinical strength of your findings.

3. Methodological Details

• Important technical details remain missing, such as sampling frequency, calibration procedures, filtering algorithms, and power/battery specifications.

• These omissions make it difficult for readers to replicate or evaluate your work.

4. Novelty and Contribution

• The manuscript still reads primarily as a feasibility study. Similar smart insole and brace systems have already been reported in recent literature.

• The unique contribution of your work—beyond proof-of-concept—should be clarified more sharply, especially how your integration of FBG sensors advances the field compared to existing IMU-based or FBG-based systems.

further to some section please cite uptodate citations 

 

• Introduction (Lines 31–77, background on gait impairment & rehabilitation):

  • Cite rhythmic auditory cueing for gait rehabilitation:
    Ghai S, Ghai I, Schmitz G. Effect of rhythmic auditory cueing on parkinsonian gait. J Neuroeng Rehabil. 2018;15(1):66. PMID: 29970069.

  • This strengthens discussion of rehabilitation methods complementing wearable devices.

• Limitations of Current Wearable Devices (Lines 50–90):

  • Cite proprioceptive interventions: PMID: 38890668. and influence of taping on force sense accuracy.  PMID: 37864268.

• Methods (Lines 107–192, describing gait parameters like dorsiflexion, inversion/eversion):

  • Cite proprioception and stability review:PMID: 28262354.

• Discussion (Lines 340–424, interpreting gait cycle findings and rehabilitation implications):

  • Relate your findings to external cueing in gait rehabilitation: PMID: 29970069.

• Conclusion (Lines 426–435):

  • When discussing tele-rehabilitation integration, you could mention broader training strategies: PMID: 40840566 (relevant for general mind-body rehabilitation context).

5. Writing and Presentation

• Some sections (e.g., detailed gait terminology, finite element modeling background) are overly long and read like textbook explanations. These dilute the focus from your experimental contributions.

• Figures are schematic but lack quantitative plots with comparative values, error bars, or statistical metrics.

• Formatting issues remain, such as duplication in author affiliations and spacing inconsistencies.

Author Response

Please see the attachment