Validation of In-Shoe Force Sensors during Loaded Walking in Military Personnel

Round 1

Reviewer 1 Report

In this study, a serious of experiments are performed to verify the validity of the loadsol® ground reaction forces sensor with simulative military conditions where soldiers carry heavy loads and walk on different gradients roads. 8 variables are compared between loadsol® and instrumented treadmill in the experiments, such as impact peak force, active peak force and stance time. The validity of the sensor is assessed with Bland–Altman 19 plots and 95% Limits of Agreement. However, several concerns in the current stage of manuscript need to be addressed, as follows: 

1. I suggest that gender and shoe size should be complemented in Table 1.

2. Why is the width of the light red and light blue curves so large in Figure 3&4? Authors should explain it in the manuscript?

3. The experiment conditions of different loads and road surfaces should be indicated in the headings of Figure 3&4.

4. It is better to define or illustrate these variables in figures, such as impulse, loading rate, stance time, and step time.

5. The manuscript focuses on assessing the performance of a commercial sensor. So, what’s the innovation of this work comparing with the authors previous research work?

6. Authors should mention some significant wearable sensors system, such as Cell Reports Physical Science 2023, 4:101191.

need to be minor revised.

Author Response

In this study, a serious of experiments are performed to verify the validity of the loadsol® ground reaction forces sensor with simulative military conditions where soldiers carry heavy loads and walk on different gradients roads. 8 variables are compared between loadsol® and instrumented treadmill in the experiments, such as impact peak force, active peak force and stance time. The validity of the sensor is assessed with Bland–Altman 19 plots and 95% Limits of Agreement. However, several concerns in the current stage of manuscript need to be addressed, as follows: 

>> Response: Thank you for your positive comments and constructive feedback. We have addressed each of your points below, and highlighted the changes in red font in the revised manuscript. We hope you find our revision satisfactory.

 

1. I suggest that gender and shoe size should be complemented in Table 1.

>> Response: Thank you for the suggestion. We have now included in the Table 1 caption that all participants are males. We have added a row on shoe size (European) – mean (SD) = 41.5 (1.5), range = 40 to 45.

 

2. Why is the width of the light red and light blue curves so large in Figure 3&4? Authors should explain it in the manuscript?

>> Response: The shaded areas in the ensemble mean and standard deviation (SD) graphs indicated the spread among the 8 participants. The large inter-individual differences in the GRF among the participants, as reflected by the SD of the graphs, could be due to variations in the magnitude of the forces or the timing at which the peak forces occurred. We have since elaborated this point in Methods, Results, Figure 3 and Figure 4 captions and Discussion.

Change in Methods:

“Ensemble mean and standard deviation (SD) time series graphs of the normal GRF were calculated from the 8 participants for each walking condition.”

Change in Results:

“The spread of the ensembled mean (SD) graphs were quite large, indicating considerable inter-individual differences in the GRF patterns among the 8 participants.”

“Figure 3. Comparison of the ensemble mean (solid line) and standard deviation (shaded region) of normal ground reaction forces (left foot contacts) concurrently measured using the loadsol® in-shoe sensors (red) and the Bertec (blue) instrumented treadmill during walking on different gradients (flat, inclined, decline) while carrying different loads (25 kg, 35 kg). Data are time normalised to the stance phase (0-100%).”

 

“Figure 4. Comparison of the ensemble mean (solid line) and standard deviation (shaded region) of normal ground reaction forces (right foot contacts) concurrently measured using the loadsol® in-shoe sensors (red) and the Bertec (blue) instrumented treadmill during walking on different gradients (flat, inclined, decline) while carrying different loads (25 kg, 35 kg). Data are time normalised to the stance phase (0-100%).”

Change in Discussion:

“The large inter-individual differences in the GRF among the participants, as reflected by the SD of the graphs, could be due to variations in the magnitude of the forces or the timing at which the peak forces occurred.”

 

3. The experiment conditions of different loads and road surfaces should be indicated in the headings of Figure 3&4.

>> Response: Thank you for the suggestion. We have expanded the captions of Figure 3 and Figure 4 to include the different load and gradient conditions: “ … during walking on different gradients (flat, inclined, decline) while carrying different loads (25 kg, 35 kg) …”.

 

4. It is better to define or illustrate these variables in figures, such as impulse, loading rate, stance time, and step time.

>> Response: We have added the definition of the extracted variables as suggested.

Change in Methods:

“Loading rate, defined as the speed at which forces impact the body, was calculated using the 20 – 80% region of the phase from touchdown to impact peak force.”

 

“Other variables extracted for both lower limbs included the stance time (duration from a foot first touches the ground to the same foot leaves the ground), stride time (duration between the first contact of a foot and the following contact of the same foot), and impulse of the stance phase (area under the force-time graph).”

 

“Step length was then calculated by dividing the walking speed by step frequency (derived from cadence), representing the average distance between the point of initial contact of one foot and the point of initial contact of the contralateral foot.”

 

5. The manuscript focuses on assessing the performance of a commercial sensor. So, what’s the innovation of this work comparing with the authors previous research work?

>> Response: Our team was not involved in the development of the sensors or validity studies under other conditions (e.g. sports, unloaded locomotion). This paper focused on the validity of the loadsol® sensors in preparation for future application on the sensors in military context. We have previously used the loadsol® sensors in a field study to compare different combat boots during walking while carrying a 20-kg field pack (Yeo et al., 2022). This earlier study, however, did not check the validity of the sensors under heavy load situation and assumed that the sensors were accurate. The present study provided empirical data to show high accuracy of the loadsol® sensors under loaded conditions and on steep slopes. This reassures it is suitable to apply the loadsol® sensors to measure in-shoe forces during loaded walking, as performed in the previous work on combat boots (Yeo et al., 2022). We have added this point in the Discussion.

Change in Discussion:

“In the military context, one previous study used the loadsol® sensors to compare in-shoe forces between different types of combat boots [7]. This earlier study included a loaded walking condition when participated carried a 20-kg field pack but did not check the validity of the sensors under heavy load situation. The present study provided empirical data to show high accuracy of the loadsol® force sensors during loaded walking. This reassures that it is appropriate to insert the loadsol® force insoles into combat boots to measure in-shoe forces during loaded walking, as performed in the previous study [7]. Having established the of validity of this commercially available system will also open up opportunities for other military applications such as monitoring the gait characteristics during a roach march.”

 

6. Authors should mention some significant wearable sensors system, such as Cell Reports Physical Science 2023, 4:101191.

>> Response: Thank you for the suggestion. We have added under Introduction some examples of wearables in the military context.

Change in Introduction:

“Recent advancement in wearable technology has opened new opportunities for biomechanical and physiological evaluation, including applications in combat boots [7,11]. Different types of technology have shown promising results in the military context, for example, smart clothing for health and safety monitoring [12], wearables for 24-hour heart rate variability monitoring [13], in-shoe plantar pressure measurements systems for gait analysis [11], and instrumented sock to provide real-time audio biofeedback for gait re-training in injured military service members [14].”

Author Response File: Author Response.pdf

Reviewer 2 Report

I like the idea and the whole paper. It is clearly and legibly written. On the other hand, you need to add an explanation of several elements that I have listed below. 

 

1. Why were 25 kg and 35kg chosen? Why wasn't the body weight percentage selected? It would be helpful to write in the introduction how and if this has been done in other works. It also needs to be explained in the methodology.
2. I have concerns about the validation of eight people. There was no power test or effect size test. The authors obviously raised this issue in the limitations, and it is good that they are aware of it. Nevertheless, please add information on effect size.
3. In the Table 1, it is worth mentioning the size of the shoes insoles.
4. How were the data from the insoles and Bertec treadmill synchronized?
5. The results chapter is poorly presented. Each figure and table should be commented on. In this layout, it is not clear what is important. It is worth moving some sentences from the discussion to the results section.

Author Response

I like the idea and the whole paper. It is clearly and legibly written. On the other hand, you need to add an explanation of several elements that I have listed below. 

 >> Response: Thank you for your positive comments and constructive feedback. We have addressed each of your points below, and highlighted the changes in red font in the revised manuscript. We hope you find our revision satisfactory.

1. Why were 25 kg and 35kg chosen? Why wasn't the body weight percentage selected? It would be helpful to write in the introduction how and if this has been done in other works. It also needs to be explained in the methodology.

 

>> Response: Thank you for raising this point. The use of fixed loads instead of body weight percentages is common in military research because most equipment weights are standard and cannot be adjusted to the soldiers’ physical characteristics. The 25-kg and 35-kg loads chosen in the present study closely reflect the loads typically used in SAF training and operations in Singapore. We have added some examples of military load carriage in the Introduction. We have also provided detailed explanation in the methodology on why fixed load carriage instead of body weight percentage was used.

 

Change in Introduction:

 

“There exist many types of military load carriage systems that are designed for specific purposes [19]. Regardless of the body size of the individuals, soldiers often carry standard weight military equipment such as backpacks, Kevlar helmets and rifles [1,7,19,20].”

 

Change in Methods:

 

“The use of fixed loads instead of body weight percentages is common in military research because most equipment weights are standard and cannot be adjusted to the soldiers’ physical characteristics. The 25-kg and 35-kg loads chosen in the present study closely reflect the loads typically used in SAF training and operations in Singapore. These loads are comparable to other military studies in different countries, for example, Chatterjee et al. [21] used fixed loads of 10.7 kg, 21.4 kg, and 30 kg to examine Indian Army soldier’s performance in high mountain while Lange et al. [20] used a standard 20-kg backpack and an assault rifle across all Swiss Army recruits.”

 

 

1. I have concerns about the validation of eight people. There was no power test or effect size test. The authors obviously raised this issue in the limitations, and it is good that they are aware of it. Nevertheless, please add information on effect size.

 

>> Response: We noted your concern about the small sample size which we have acknowledged in the limitation. Since we do not intend to run inferential statistics such as t-tests and ANOVA for hypothesis testing, the use of power analysis and effect size are not suitable. For validity assessment, one of the most accepted methods in the field is to use Bland Altman plots and 95% Limits of Agreement. We follow this current best practice in our statistical analysis approach which has been widely also used in other validation studies on the loadsol® system (Peeble et al., 2018; Renner et al., 2019; Seiberl et al., 2018). Reference supports are added in the manuscript.

 

Change in text:

“This statistical analysis approach has been widely used in other validation studies including those specifically on the loadsol® system [16–18].”

 

 

1. In the Table 1, it is worth mentioning the size of the shoes insoles.

>> Response: Thank you for the suggestion. We have added a row in Table 1 on the participants’ shoe sizes (European): mean (SD) = 41.5 (1.5), range = 40 to 45. The loadsol® insole sizes were matched to the participants’ shoe sizes.

 

1. How were the data from the insoles and Bertec treadmill synchronized?

 

>> Response: Data from the loadsol® insoles and the Bertec treadmill were not collected in a synchronized manner. Both systems were manually triggered to start recording at approximately the same time. The precise matching of data was done offline based on the heel strike and toe-off events using a detection threshold of 40 N in the normal ground reaction force data. We have now clarified this in the revised manuscript.

 

Change in Methods:

 

“At the 9th-minute mark of each walking bout, in-shoe loadsol® and treadmill GRF were concurrently collected for 30 seconds. This was achieved by manually triggering both systems to start recording at approximately the same time. While the two systems were not completely synchronized during data collection, precise matching of the data was done offline based on the heel strike and toe-off events detected from GRF measurements.”

 

1. The results chapter is poorly presented. Each figure and table should be commented on. In this layout, it is not clear what is important. It is worth moving some sentences from the discussion to the results section.

 

>> Response: Thank you for the suggestion. We have since elaborated on the figures and tables resultsb with comments to highlight the key findings. To improve flow and clarity, we have re-arranged some text and figures in the Results session. Some sentences in the Discussion were to Results to avoid duplication.

Change in Results:

 

“The Bland-Altman plots for kinetic and spatiotemporal variables across all load and gradient conditions are shown in Figure 5 and Figure 6, respectively. The corresponding mean (SD) values, bias and 95% LoA are tabulated in Table 2 (flat surface), Table 3 (inclined surface) and Table 4 (declined surface). The bias of most kinetic and spatiotemporal variables was generally low, with a narrow range of LoA. These results indicate high accuracy and good agreement of the loadsol® system with standard laboratory equipment. One interesting observation was during inclined walking, loadsol® tended to under-estimate the impact peak force, but not during flat or declined walking. This trend was consistent for both 25 kg (Figure 3) and 35 kg (Figure 4) conditions. The mean bias of the impact peak force during inclined walking (183.4 to 188.6 N) were much higher than those under flat (- 25.9 to -45.8 N) and declined (29.2 to 70.3 N) conditions (Tables 2 to 4).”

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

1. The author should highlight the innovation of this manuscript.

2. The manuscript should illustrates the circuit signal process system of the force sensors. I suggest the manuscript imitate and cite this significant wearable sensors system literatrue, Cell Reports Physical Science 2023, 4:101191.

 

Minor revise.

Author Response

1. The author should highlight the innovation of this manuscript.

>> Response: We have further highlighted the novelty of the manuscript. The key changes in the Discussion are:

“To the authors’ best knowledge, this is first study to illustrate the mean ensemble GRF-time graphs obtained from loadsol® sensors in comparison with laboratory standard equipment.”

“There are no reported validity data on stride time, cadence, or step length in the literature for direct comparison. The lack of previous validity data on key spatiotemporal variables highlighted the novelty of the present study to comprehensively evaluate the loadsol® sensors.”

 

2. The manuscript should illustrates the circuit signal process system of the force sensors. I suggest the manuscript imitate and cite this significant wearable sensors system literatrue, Cell Reports Physical Science 2023, 4:101191.

>> Response: We have since added the recommended paper in the manuscript and added it in the reference list.

Change in text:

“The current loadsol® system requires a mobile device, such as a mobile phone or an ipad, to be placed in close proximity for data acquisition. Future innovation may explore ways to wirelessly transfer real-time data via cloud terminal and to comprehensively extract meaningly parameters for health and performance monitoring [27].”

New Reference added:

27. Li, Y., Liu, C., Zou, H., Che, L., Sun, P., Yan, J., Liu, W., Xu, Z., Yang, W., Dong, L. and Zhao, L. Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring. Cell Rep. Phys. Sci., 2023, 4, 101191. doi:10.1016/j.xcrp.2022.101191

Author Response File: Author Response.pdf