58 research outputs found
COMBINING MUSCULOSKELETAL MODELING AND FEM IN DIABETIC FOOT PREVENTION
No full textRecently the development of Patient-specific models (PSMs) tailored to patient-specific data, has gained more and more attention in clinical applications. PSMs could represent a solution to the growing awareness of personalized medicine which allow the realization of more effective rehabilitation treatments designed on the subject capabilities. PSMs have the potential of improving diagnosis and optimizing clinical treatments by predicting and comparing the outcomes of different approaches of intervention. Furthermore they can provide information that cannot be directly measured, such as muscle forces or internal stresses and strains of the bones.
Given the considerable amount of diseases affecting motor ability, PSMs of the lower limbs have been broadly addressed in literature. Two techniques are mostly used in this area: musculoskeletal (MS) modeling and finite element (FE) analysis.
(MS) models represent a valuable tool, as they can provide important information about the unique anatomical and functional characteristics of different subjects, through the computation of human internal variables, such as muscle activations and forces and joint contact forces.
The flexibility and adaptability of FE analysis makes it a perfect solution to model biological geometries and materials and to simulate complicated boundary and loading conditions. Accurate and descriptive FE models would serve as an excellent tool for scientific and medical research. Furthermore they could be used in clinical settings if combined with medical imaging, in order to improve patient care.
Several 3-dimensional (3D) foot FE models were recently developed to analyze the biomechanical behavior of the human foot and ankle complex that is commonly studied with experimental techniques like stereophotogrammetry, force and plantar pressure plates.
In this context, many gait analysis protocols have been proposed to assess the 3D kinetics, kinematics and plantar pressure distribution. This evaluation has shown to be useful in characterizing the foot biomechanics in different pathologies like the diabetic foot.
Diabetic foot is an invalidating complication of diabetes mellitus, a chronic disease frequently encountered in the aging population. It is characterize by the development of ulcers which can lead to amputation.
Models for simulations of deformations and stresses in the diabetic plantar pad are required to predict high risk areas on the plantar surface and can be used to investigate the performance of different insoles design for optimal pressure relief.
This work represents a first effort towards the definition of a more complete PSM which combining both a MS model and a FE model, can increase the understanding of the diabetic foot pathology. To achieve this objective, several limitations and issues have been addressed.
As first, MS models of diabetic and control subjects were developed using OpenSim, to estimate muscle forces. The objective was to evaluate whether the diabetic population exhibit lower limb muscle strength deficits compared to the healthy one. Subjects routine gait analysis was performed and lower limb joints kinematics, kinetics, time and space parameters estimated by means of a modified version of the IORgait protocol. 3D lower limb joints kinematics and kinetics was also calculated with OpenSim. Both methodologies were able to highlight differences in joint kinematics and kinetics between the two populations. Furthermore MS models showed significant differences in healthy muscle forces with respect to the diabetic ones, in some of the muscles. This knowledge can help the planning of specific training in order to improve gait speed, balance, muscle strength and joint mobility.
After the use of MS models proved to be applicable in the diabetic population, the next step was to combine them with foot FE models. This was done in two phases.
At first the impact of applying the foot joints contact forces (JCFs) obtained from MS models as boundary condition on the foot FE models was verified. Subject specific geometries from MRI were used for the development of the foot FE models while the experimental plantar pressures acquired during gait were used in the validation process. A better agreement was found between experimentally measured and simulated plantar pressure obtained with JCFs than with the experimentally measured ground reaction forces as boundary conditions.
Afterwards the use of muscles forces as boundary condition in the FE simulations was evaluated. Subject-specific integrated and synchronized kinematic-kinetic data acquired during gait analysis were used for the development of the MS models and for the computation of the muscle forces. Muscle insertions were then located in the MRI and correspondent connectors were created in the FE model.
FE subject-specific simulations were subsequently run with Abaqus by conducting a quasi-static analysis on 4 gait cycle phases and adopting 2 conditions: one including the muscle forces and one without. Once again the validation of the FE simulations was done by means of a comparison between simulated and experimentally measured plantar pressures. Results showed a marked improvement in the estimation of the peak pressure for the model that included the muscles.
Finally, an attempt towards the definition of a parametric foot finite element model was done. In fact, despite the recent developments, patient-specific models are not yet successfully applied in a clinical setting. One of the challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) can represent an appealing solution. In this study PCA was applied to the feet of a small cohort of diabetic and healthy subjects in order to evaluate the possibility of developing parametric foot models and to use them to identify variations and similarities between the two populations.
The limitations of the use of models have also been analyzed. Their adoption is indeed limited by the lack of verification and validation standards. Even using subjects’ MRI or CT data for the development of FEM together with experimentally acquired motion analysis data for the boundary and loading conditions, the subject specifity is still not reached for what regards all the material properties. Furthermore it should be considered that everything relies on algorithm and models that would never be perfectly representing the reality.
Overall, the work presented in this thesis represents an extended evaluation of the possible uses of modeling techniques in the diabetic foot prevention, by considering all the limitations introduced as well as the potential benefits of their use in a clinical context. The research is organized in six chapters:
Chapter 1 - provides a background on the modeling techniques, both FE modeling and MS modeling. Furthermore it also describes the gait analysis, its instrumentation and some of the protocols used in the evaluation of the biomechanics of the lower limbs;
Chapter 2 - gives a detailed overview of the biomechanics of the foot. It particularly focuses on the diabetes and the diabetic foot;
Chapter 3 - introduces the application of MSs for the diabetic foot prevention after a brief background on the techniques usually chosen for the evaluation of the motor impairments caused by the disease. Aim, material and methods, results and discussion are presented. The complete work flow is described, and the chapter ends with a discussion on new key findings and limitations.
Chapter 4 – reports the work done to combine the use of musculoskeletal models with foot FEMs. At first the impact of applying the foot joints contact forces obtained from MS models as boundary condition on the foot FEMs is verified. Then the use of muscles forces (again obtained from MS models) as boundary condition in the FE simulations is evaluated. For both studies a brief background is presented together with the methods applied, the results obtained and a discussion of novelties and drawbacks.
Chapter 5 – explores the possibility of defining a parametric foot FEM applying the Principle Component Analysis (PCA) on the feet of a small cohort of diabetic and healthy subjects. A background on the importance of patient specific models is presented followed by material and methods, results and discussion of what obtained with this study.
Chapter 6 - summarizes the results and the novelty of the thesis, delineating the conclusions and the future research paths
Towards the generation of a parametric foot model using principal component analysis: A pilot study
No full textSubject-specific modelling of the foot integrating finite element modelling, gait analysis and opensim: proof of concept in diabetic neuropathic subjects
No full textQuantitative and qualitative running gait analysis through an innovative video-based approach
No full textQuantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait-the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel's brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland-Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r2 = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r2 = 0.98). Bland-Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r2 = 0.00038; 0.21 ± 0.96, p = 0.87, r2 = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals
Estimating Muscle Power of the Lower Limbs through the 5-Sit-to-Stand Test: A Comparison of Field vs. Laboratory Method
No full textThe 5-Sit-to-stand test (5STS) is used for lower limb muscle power (MP) determination in field/clinical setting. From the time taken to perform five standing movements and three partially verified assumptions (vertical displacement, mean concentric time, and mean force), MP is estimated as the body’s vertical velocity x force. By comparison with a gold standard, laboratory approach (motion capture system and force plate), we aimed to: (1) verify the assumptions; (2) assess the accuracy of the field-estimated MP (MPfield); (3) develop and validate an optimized estimation (MPfield-opt). In 63 older adults (67 ± 6 years), we compared: (i) estimated and measured assumptions (2-WAY RM ANOVA), (ii) MPfield and MPfield-opt with the reference/laboratory method (MPlab) (2-WAY RM ANOVA, Pearson’s correlation coefficient (r), Bland-Altman analysis). There was a significant difference between estimated and measured assumptions (p < 0.001). Following the implementation of the optimized assumptions, MPfield-opt (205.1 ± 55.3 W) was not significantly different from Mlab (199.5 ± 57.9 W), with a high correlation (r = 0.86, p < 0.001) and a non-significant bias (5.64 W, p = 0.537). Provided that corrected assumptions are used, 5STS field test is confirmed a valid time- and cost-effective field method for the monitoring of lower limbs MP, a valuable index of health status in aging
Temporal, Kinematic and Kinetic Variables Derived from a Wearable 3D Inertial Sensor to Estimate Muscle Power during the 5 Sit to Stand Test in Older Individuals: A Validation Study
No full text: The 5-Sit-to-stand test (5STS) is widely used to estimate lower limb muscle power (MP). An Inertial Measurement Unit (IMU) could be used to obtain objective, accurate and automatic measures of lower limb MP. In 62 older adults (30 F, 66 ± 6 years) we compared (paired t-test, Pearson's correlation coefficient, and Bland-Altman analysis) IMU-based estimates of total trial time (totT), mean concentric time (McT), velocity (McV), force (McF), and MP against laboratory equipment (Lab). While significantly different, Lab vs. IMU measures of totT (8.97 ± 2.44 vs. 8.86 ± 2.45 s, p = 0.003), McV (0.35 ± 0.09 vs. 0.27 ± 0.10 m∙s-1, p < 0.001), McF (673.13 ± 146.43 vs. 653.41 ± 144.58 N, p < 0.001) and MP (233.00 ± 70.83 vs. 174.84 ± 71.16 W, p < 0.001) had a very large to extremely large correlation (r = 0.99, r = 0.93, and r = 0.97 r = 0.76 and r = 0.79, respectively, for totT, McT, McF, McV and MP). Bland-Altman analysis showed a small, significant bias and good precision for all the variables, but McT. A sensor-based 5STS evaluation appears to be a promising objective and digitalized measure of MP. This approach could offer a practical alternative to the gold standard methods used to measure MP
Testing the performance of an innovative markerless technique for quantitative and qualitative gait analysis
No full textGait abnormalities such as high stride and step frequency/cadence (SF-stride/second, CAD-step/second), stride variability (SV) and low harmony may increase the risk of injuries and be a sentinel of medical conditions. This research aims to present a new markerless video-based technology for quantitative and qualitative gait analysis. 86 healthy individuals (mead age 32 years) performed a 90 s test on treadmill at self-selected walking speed. We measured SF and CAD by a photoelectric sensors system; then, we calculated average ± standard deviation (SD) and within-subject coefficient of variation (CV) of SF as an index of SV. We also recorded a 60 fps video of the patient. With a custom-designed web-based video analysis software, we performed a spectral analysis of the brightness over time for each pixel of the image, that reinstituted the frequency contents of the videos. The two main frequency contents (F1 and F2) from this analysis should reflect the forcing/dominant variables, i.e., SF and CAD. Then, a harmony index (HI) was calculated, that should reflect the proportion of the pixels of the image that move consistently with F1 or its supraharmonics. The higher the HI value, the less variable the gait. The correspondence SF-F1 and CAD-F2 was evaluated with both paired t-Test and correlation and the relationship between SV and HI with correlation. SF and CAD were not significantly different from and highly correlated with F1 (0.893 ± 0.080 Hz vs. 0.895 ± 0.084 Hz, p < 0.001, r2 = 0.99) and F2 (1.787 ± 0.163 Hz vs. 1.791 ± 0.165 Hz, p < 0.001, r2 = 0.97). The SV was 1.84% ± 0.66% and it was significantly and moderately correlated with HI (0.082 ± 0.028, p < 0.001, r2 = 0.13). The innovative video-based technique of global, markerless gait analysis proposed in our study accurately identifies the main frequency contents and the variability of gait in healthy individuals, thus providing a time-efficient, low-cost means to quantitatively and qualitatively study human locomotion
Construct Validity of a Wearable Inertial Measurement Unit (IMU) in Measuring Postural Sway and the Effect of Visual Deprivation in Healthy Older Adults
No full textInertial Motor sensors (IMUs) are valid instruments for measuring postural sway but their ability to detect changes derived from visual deprivation in healthy older adults requires further investigations. We examined the validity and relationship of IMU sensor-derived postural sway measures compared to force plates for different eye conditions in healthy older adults (32 females, 33 males). We compared the relationship of the center of mass and center of pressure (CoM and CoP)-derived total length, root means square (RMS) distance, mean velocity, and 95% confidence interval ellipse area (95% CI ellipse area). In addition, we examined the relationship of the IMU sensor in discriminating between open- (EO) and closed-eye (EC) conditions compared to the force plate. A significant effect of the instruments and eye conditions was found for almost all the variables. Overall, EO and EC variables within (force plate r, from 0.38 to 0.78; IMU sensor r, from 0.36 to 0.69) as well as between (r from 0.50 to 0.88) instruments were moderately to strongly correlated. The EC:EO ratios of RMS distance and 95% CI ellipse area were not different between instruments, while there were significant differences between total length (p = 0.973) and mean velocity (p = 0.703). The ratios’ correlation coefficients between instruments ranged from moderate (r = 0.65) to strong (r = 0.87). The IMU sensor offers an affordable, valid alternative to a force plate for objective, postural sway assessment
Feasibility and effectiveness of a 6-month, home-based, resistance exercise delivered by a remote technological solution in healthy older adults
No full textBackground: Aging is characterized by a physiological decline in physical function, muscle mass, strength, and power. Home-based resistance training interventions have gained increasing attention from scientists and healthcare system operators, but their efficacy is yet to be fully determined. Aims: to verify the safety, feasibility, and efficacy of a home-based resistance training program delivered by innovative technological solution in healthy older adults. Methods: 73 participants (36 females) were randomly allocated to either a control (C) or an intervention (I) group consisting of a 6-months home-based resistance training program delivered through an innovative technological solution, which included a wearable inertial sensor and a dedicated tablet. The safety and feasibility of the intervention were assessed by recording training-related adverse events and training adherence. Body composition, standing static balance, 10-meter walking, and loaded 5 sit-to-stand tests were monitored to quantify efficacy. Results: No adverse events were recorded. Adherence to the training program was relatively high (61 % of participants performed the target 3 sessions) in the first trimester, significantly dropping during the second one. The intervention positively affected walking parameters (p < 0.05) and maximal force (p = 0.009) while no effect was recorded on body composition, balance, and muscle power. Conclusions: The home-based device-supported intervention was safe and feasible, positively affecting walking parameters and lower limbs' maximal force. This approach should be incentivized when barriers to participation in traditional resistance exercise programs are present
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