Development of an Anisotropic Hyperelastic Material Model for Porcine Colorectal Tissues

Many colonic surgeries include colorectal anastomoses whose leaks may be life-threatening, affecting thousands of patients annually. Various studies propose that mechanical interaction between the staples and neighboring tissues may play an important role in anastomotic leakage. Therefore, understanding the mechanical behavior of colorectal tissue is essential to characterizing the reasons for this type of failure. So far, experimental data characterizing the mechanical properties of colorectal tissue have been few and inconsistent, which has significantly limited understanding their behavior. This research proposes an approach to developing an anisotropic hyperelastic material model for colorectal tissues based on uniaxial testing of freshly harvested porcine specimens, which were collected from several age- and weight-matched pigs. The specimens were extracted from the same colon tract of each pig along their circumferential and longitudinal orientations. We propose a constitutive model combining Yeoh isotropic hyperelastic material with fibers oriented in two directions to account for the hyperelastic and anisotropic nature of colorectal tissues. Experimental data were used to accurately determine the model's coefficients (circumferential, R2 = 0.9968; longitudinal, R2 = 0.9675). The results show that the proposed model can be incorporated into a finite element model that can simulate procedures such as colorectal anastomoses reliably.

Automated plantar contact area estimation in a dynamic state using K-Means clustering

BACKGROUND

Estimation of plantar contact area (PCA) can be used for a variety of purposes such as classification of foot types and diagnosis of foot abnormalities. While some techniques have been developed for assessing static PCA, understanding dynamic PCA may improve understanding of gait biomechanics. This study aims (1) to develop an approach to estimate PCA from video images of footprints during walking and (2) to assess the accuracy and generalizability of this method.

METHODS

A sample of 41 ambulatory, young adults (age = 24.3 ± 3.2 years, mass = 67.2 ± 16.9 kg, height = 1.63 ± 0.08 m) completed 10 trials walking on a raised transparent plexiglass platform. Foot contact during walking was recorded using a video camera placed under the platform. An image processing algorithm, Clustering Segmentation, was developed based on identifying color intensity between the PCA and the rest of the foot and plantar contact morphology.

RESULTS

The proposed approach was compared to manual hand tracing, which is widely accepted as the Gold Standard, as well as with an earlier automated approach (Lidstone et al., 2019). Results showed that Clustering Segmentation followed the Gold Standard closely in all phases of gait. The maximum PCA and the maximum PCA length and width generally increased with foot size, indicating that the algorithm could successfully estimate the PCA across a wide range of foot sizes. Results also showed that the proposed approach for obtaining the PCA may be used to characterize various foot types in a dynamic state.

CONCLUSION

Clustering Segmentation algorithm eliminates the need for subjective interpretation of the PCA. The results showed that the algorithm was considerably faster and more accurate than the earlier automated method. The proposed algorithm will be appropriate for assessment of foot abnormalities and provides complementary information to gait analysis.

Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning

Diabetic peripheral neuropathy (DN) is a serious complication of diabetes mellitus (DM) that can lead to foot ulceration and eventual amputation if not treated properly. Therefore, detecting DN early is important. This study presents an approach for diagnosing various stages of the progression of DM in lower extremities using machine learning to classify individuals with prediabetes (PD; n = 19), diabetes without (D; n = 62), and diabetes with peripheral neuropathy (DN; n = 29) based on dynamic pressure distribution collected using pressure-measuring insoles. Dynamic plantar pressure measurements were recorded bilaterally (60 Hz) for several steps during the support phase of walking while participants walked at self-selected speeds over a straight path. Pressure data were grouped and divided into three plantar regions: rearfoot, midfoot, and forefoot. For each region, peak plantar pressure, peak pressure gradient, and pressure-time integral were calculated. A variety of supervised machine learning algorithms were used to assess the performance of models trained using different combinations of pressure and non-pressure features to predict diagnoses. The effects of choosing various subsets of these features on the model's accuracy were also considered. The best performing models produced accuracies between 94-100%, showing the proposed approach can be used to augment current diagnostic methods.

Use of Pressure-Measuring Insoles to Characterize Gait Parameters in Simulated Reduced-Gravity Conditions

Prior researchers have observed the effect of simulated reduced-gravity exercise. However, the extent to which lower-body positive-pressure treadmill (LBPPT) walking alters kinematic gait characteristics is not well understood. The purpose of the study was to investigate the effect of LBPPT walking on selected gait parameters in simulated reduced-gravity conditions. Twenty-nine college-aged volunteers participated in this cross-sectional study. Participants wore pressure-measuring insoles (Medilogic GmBH, Schönefeld, Germany) and completed three 3.5-min walking trials on the LBPPT (AlterG, Inc., Fremont, CA, USA) at 100% (normal gravity) as well as reduced-gravity conditions of 40% and 20% body weight (BW). The resulting insole data were analyzed to calculate center of pressure (COP) variables: COP path length and width and stance time. The results showed that 100% BW condition was significantly different from both the 40% and 20% BW conditions, p < 0.05. There were no significant differences observed between the 40% and 20% BW conditions for COP path length and width. Conversely, stance time significantly differed between the 40% and 20% BW conditions. The findings of this study may prove beneficial for clinicians as they develop rehabilitation strategies to effectively unload the individual's body weight to perform safe exercises.

Enhancing the Accuracy of Vertical Ground Reaction Force Measurement During Walking Using Pressure-Measuring Insoles

Pressure-measuring insoles can be an attractive tool for measuring ground reaction force (GRF) since they are portable and can record multiple consecutive steps. Several researchers have, however, observed that these insoles are less accurate than instrumented force platforms. To address this issue, the authors identified transfer functions that best described each insole size to enhance the measurements of the vertical component of GRF during walking. GRF data were collected from 29 participants (6/23 male/female, 24.3 ± 6.7 yrs, 70.4 ± 23.9 kg, 1.66 ± 0.11 m) using Medilogic® pressure-measuring insoles and Kistler® force platforms for three walking trials. Participants provided the institutionally approved written consent (IRB #724468). The data from both instruments were preprocessed. A subset of the data was used to train the system identification toolbox (matlab) to identify the coefficients of several candidate transfer functions for each insole size. The resulting transfer functions were compared using all available data for each insole to assess which one modified the insole data to be closer to that of the force platform. All tested transfer functions moved the vertical component of GRF closer to the corresponding force platform data. Each insole size had a specific transfer model that that yielded the best results. Using system identification techniques produced transfer functions that, when using insole data of the vertical component of GRF as input, produced output that is comparable to the corresponding measurement using an instrumented force platform.

A comparison of two techniques for center of pressure measurements

Introduction

Force platforms and pressure-measuring insoles are the most common tools used for measuring center of pressure. Earlier studies to assess these instruments suffered from limited sample sizes or an inadequate range of participant foot sizes. The purpose of this study was to propose new methods to extract and calculate comparably accurate center of pressure for the Kistler® force platform and Medilogic® insoles.

Methods

Center of pressure data were collected from 65 participants wearing pressure-measuring insoles (six different sizes). Participants walked over consecutive force platforms for three trials while wearing pressure-measuring insoles within socks. Onset force thresholds and center of pressure segment length thresholds were used to determine accurate center of pressure path length and width. A single step for each foot and trial was extracted from both instruments.

Results

A strong correlation was observed between instruments in center of pressure length (4.12 ± 6.72% difference, r = 0.74). Center of pressure width varied and was weakly correlated (–7.04 ± 4.48% difference, r = 0.11).

Conclusions

The results indicate that both instruments can measure center of pressure path length consistently and with comparable accuracy (differences < 10%). There were differences between instruments in measuring center of pressure path width, which were attributed to the limited number of sensors across the width of the insoles.

Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking

BACKGROUND

Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography.

METHODS

Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact.

RESULTS

The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78).

CONCLUSIONS

The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.

Electronic measurement of plantar contact area during walking using an adaptive thresholding method for Medilogic® pressure-measuring insoles

BACKGROUND

Pressure-measuring insoles have the potential to measure plantar contact area (PA) during walking. However, they are not widely used for this purpose because of the need for a reliable process that can convert the insole output into PA. The purposes of this study were to: (1) develop an adaptive-threshold method for pressure-measuring insoles that can improve the accuracy of the PA measurements during walking, and (2) experimentally assess the accuracy and generalizability of this method.

METHODS

A sample of 42 healthy, ambulatory, young adults (age=24.3±3.2years, mass=67.2±16.9kg, height=1.63±0.08m) completed 10 trials walking on an elevated walkway while wearing Medilogic^® pressure-measuring insoles (sizes 35-45). A total of six insole sizes were considered. Insole data were converted to PA using three unique adaptive-thresholds that were based on percentages of the maximum sum of digital values (MSDV) during an analyzed step. Three values were considered: 0.1%, 0.2%, and 0.3% of the MSDV. Additionally, a fixed-threshold, which is typically used to estimate PA, was assessed. These two techniques, applied to the insole worn on the left foot, were compared with PA obtained from high-resolution reference footprints obtained from optical pedography of the right foot and processed using digital image processing algorithms. An assumption of PA symmetry between the left (insole) and right (barefoot) feet was made and comparisons were conducted over the entire stance phase of walking. The generalizability of the algorithm was assessed by comparing PA errors from insoles with respect to the optical pedography results based on insole size criteria.

RESULTS

The adaptive-thresholds of 0.1%, 0.2%, and 0.3% of MSDV produced mean errors of 7.31±17.44%, -8.62±15.01%, and -20.45±14.18%, respectively. Using the 2-digital value fixed-threshold produced a mean error of 20.88±22.44%. The best performing adaptive-threshold varied among insole sizes.

CONCLUSION

It was observed that the fixed-threshold technique produced large magnitudes of errors. The proposed adaptive-thresholds of 0.1% and 0.2% of the MSDV reduced PA error to ±10% during walking. The adaptive-threshold method consistently reduced PA error vs. the fixed-threshold for each insole size.

Assessing the validity of pressure-measuring insoles in quantifying gait variables

Introduction

Pressure-measuring insoles can provide a portable alternative to existing gait analysis tools. However, there is disagreement among researchers on their accuracy and the appropriate calibration methods. The purposes of this study were to (1) determine the validity of pressure-measuring insoles for calculating stance time and support-phase impulse during walking using two calibration procedures, and (2) examine the effect of insole size on the results.

Methods

Data were collected from 39 participants (23.5 ± 3.24 yrs, 66.7 ± 17.5 kg, 1.64 ± 0.09 m), each wearing appropriately sized insoles as they walked over two consecutive force platforms. Two calibration methods were evaluated: (1) manufacturer's recommendation, and (2) a participant weight-based approach. Qualitative and quantitative evaluations were conducted.

Results

The results indicated that the insoles measured longer stance times than the force platform (differences are less than 10%). Both calibration methods resulted in inaccurate impulse values (differences are 30 and 50% for the two calibration methods, respectively). The results showed that when using the first calibration method, impulse values depended on insole size. The second calibration consistently underestimated the impulse.

Conclusions

It was concluded that while the insoles provide acceptable qualitative representation of the gait, the two studied calibration methods may lead to a misleading quantitative assessment.