Labor Biomechanik (LBM)
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Introduction
About 40 % of the women worldwide suffer from Pelvic Floor Dysfunctions [1]. In previous studies, musculoskeletal simulations helped to understand and facilitate the complex issues of musculoskeletal disorders in several different branches [2, 3]. A fundamental prerequisite for the generation of these models is to have a basic understanding of the stresses acting on the pelvic floor. The aim of this study is to investigate the biomechanical factors influencing the activity of the pelvic floor muscles by establishing a parameterised model.
Methods
To identify the factors that affect the pelvic floor muscles, the process is divided into several stages. The first step is to investigate the loads acting on the pelvic floor. For this, a simplified sagittal cross-sectional model of the upper body is analysed. The discretization of the abdominal cavity is presented with resulting force vectors representing the organs in the abdominal and pelvic region, the tensile force of the structures that fixate the organs as well as the intraabdominal pressure.
A second step is the examination of the basic mechanics of the pelvic floor muscles. As the functional anatomy of these muscles demonstrate differences compared to other muscle groups [4], the understanding of how they differ is crucial for the construction of musculoskeletal models.
After a static examination of loads on a simplified muscle model, an extended model including the physiological properties of a muscle must be involved in the considerations. Therefore, the influence of parameters such as the ideal muscle strength and fibre length, as well as elasticity or passive stiffness of the elements must be taken into account. The influencing factors of the variables can be analysed in a simplified pelvic floor muscle model by systematically changing the parameters.
Results
The process of analysing the factors influencing the activity of the pelvic floor muscles follows two main strategies. The review of the effects of the whole body identifies the forces acting on the pelvic floor muscles and effect their activity.
With the examination of the basic mechanics of a simplified pelvic floor muscle model a better understanding is achieved of how the pelvic floor muscles absorb the loads acting in the body.
Discussion
This process results in the main influence factors on the activity of the pelvic floor. The consequent parameters form the basis for a biomechanically justified construction of musculoskeletal simulation models of the pelvic floor. Nevertheless, it must be considered, that these mechanical studies represent simplifications of the reality.
References
1. Wang et al, Front Public Health, 10:975829, 2022.
2. Bulat et al., Curr Sports Med Rep, 18(6):210-216, 2019
3. Melzner et al., Eur J Obstet Gynecol Reprod Biol, 264:306-313, 2021
4. Ashton-Miller et al., Ann N Y Acad Sci, 1101:266-96, 2007
Workflow for the development of a non-invasive feedback device to assess pelvic floor contractions
(2024)
Introduction
Pelvic floor disorders affect about 40% of women worldwide [1]. Pelvic floor muscle (PFM) training is both a preventive and a therapeutic intervention. Current PFM training devices are invasive and have little scientific evidence. The idea is to develop a noninvasive feedback device to assess adequate PFM contraction. Therefore, evidence-based female musculoskeletal models, non-invasive data acquisition, sensor technology and artificial intelligence (AI) will be combined. This work presents the workflow to achieve such a feedback device and describes the interaction of the technologies used.
Methods
Exercises that induce PFM contractions have been evaluated and defined. Motion capture of these exercises will provide input for female musculoskeletal models. A combination of biomechanical rigid body and FEM simulations will be used to estimate PFM contractions. In addition, a non-invasive sensor will measure pelvic floor activity. The simulated and measured data will be used to develop an AI model that provides feedback on PFM contractions based on non-invasive data collection.
Results
The AMMR (AnyBody Managed Model Repository) of the AnyBody modelling system (AMS, Aalborg, Denmark) serves as the initial model for performing inverse dynamic simulations of the exercises. To calculate the PFM forces, the full-body model must be supplemented with the relevant pelvic floor structures and a mass model of the internal organs. A modified abdominal pressure model must also be incorporated. The AMS calculates the PFM activities caused by the internal organ loads and the generated abdominal pressure during the exercises. The muscle activities are transferred to a FEM model of the female pelvic floor (SfePy, simple finite elements in Python). The identical pelvic floor structures were integrated into the FEM model as in the AMS. Active PFM contractions can be simulated using the FEM model. Movement of the coccyx due to PFM contractions has been reported in the literature [2,3]. Therefore, a noninvasive coccyx motion sensor will be developed to provide additional information on PFM contractions. The measured data (coccyx motion sensor, motion capture) and the simulation results of the models will be combined to create an AI feedback model using Python. The final feedback device will consist of the AI model and the developed coccyx motion sensor, which can reproduce the resulting PFM contractions based on the sensor data and simplified motion tracking.
Discussion
The creation of the AMS and the FEM model is a prerequisite for the development of the feedback device. The relevant structures in the models are located inside the body. This limits the ability to observe the structures during the exercises, which can lead to difficulties in model validation. The development of a user-friendly sensor with sufficient measuring accuracy of the coccyx motion is another challenge. Nevertheless, the workflow represents a promising approach to develop a noninvasive feedback system to assess PFM contraction.
References
1. Wang et al, Front Public Health, 10:975829, 2022.
2. Bø et al, Neurourol Urodyn, 20:167–174, 2001.
3. Fujisaki et al, J Phys Ther Sci, 30:544–548, 2018.
Acknowledgements
This work was supported by the project no. BYCZ01-014 of the Program INTERREG Bavaria – Czechia 2021–2027.
Introduction
Dislocation of the shoulder joint is one of the more common complications after reverse total shoulder arthroplasty [1], which is often associated with malposition of the prosthetic components [2]. Therefore, achieving sufficient shoulder stability should not be neglected when positioning the implant components. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this work is to develop a reverse shoulder implant prototype that allows intraoperative measurement of shoulder stiffness while varying the position of the implant components. The measured stiffness could provide a quantitative statement regarding the optimal positioning of the implant components, which can be adjusted accordingly in the final reverse shoulder prosthesis.
Methods
To measure the stiffness of the shoulder joint, it is necessary to record the joint angles and the torques generated during movement. The changes in the rotation angles were measured using 3D hall sensors and magnets. The magnets were placed under the humerosocket, and the hall sensors were integrated into the glenosphere. The strength of the magnetic field was used to determine the position of the humerosocket in relation to the glenosphere. The accuracies of the angle measurements were tested using a test bench.
Three thin film pressure sensors were used to record forces at different points under the humerosocket. To obtain a force value from the sensor signal, the sensors were calibrated using a load cell. The variation of the implant components positions was integrated into the prototype implant through different constructive mechanisms to adjust the stiffness of the shoulder joint.
Results
In the range of ±45° flexion/extension combined with ±15° adduction/abduction, the joint position could be determined with sufficient accuracy (error e ≤ 5°). The areas near the combined maximum deflections of ±45° flexion/extension and ±45° adduction/abduction indicate the greatest deviation from the target angle. The force values of the thin film sensors enable the calculation of moments around two axes. As variable component position parameters, the tray offset, the neck-shaft angle and the humerus version were integrated into the implant prototype.
Discussion
Ideally, the accuracy of the angle measurements should only depend on the amount of deflection and not on the direction of deflection. The asymmetric behavior indicates a deviation from the correct positioning of the hall sensors. The application of a calibration matrix could compensate for the measurement errors and could demonstrate the potential of the new method for joint angle measurements. The accuracy of the torque measurements and the functionality of the mechanical arresting mechanisms must be investigated in further studies. Overall, the developed measurement method can help to avoid malpositioning of the implant components in reverse total shoulder arthroplasty.
References
1. Clark et al, J Shoulder and elbow surgery, 21:36-41 2012.
2. Randelli et al, J Musculoskeletal surgery, 98:15-18, 2014.
Subject Specific Scaling Of Muscle Strengths In The Lower Extremity For Musculoskeletal Models
(2024)
The success of surgical treatment for fractures hinges on various factors, notably accurate surgical indication. The process of developing and certifying a new osteosynthesis device is a lengthy and costly process that requires multiple cycles of review and validation. Current methods, however, often rely on predecessor standards rather than physiological loads in specific anatomical locations. This study aimed to determine actual loads experienced by an osteosynthesis plate, exemplified by a standard locking plate for the femoral shaft, utilizing finite elements analysis (FEA) and to obtain the bending moments for implant development standard tests. A protocol was developed, involving the creation and validation of a fractured femur model fixed with a locking plate, mechanical testing, and FEA. The model’s validation demonstrated exceptional accuracy in predicting deformations, and the FEA revealed peak stresses in the fracture bridging zone. Results of a parametric analysis indicate that larger fracture gaps significantly impact implant mechanical behavior, potentially compromising stability. This study underscores the critical need for realistic physiological conditions in implant evaluations, providing an innovative translational approach to identify internal loads and optimize implant designs. In conclusion, this research contributes to enhancing the understanding of implant performance under physiological conditions, promoting improved designs and evaluations in fracture treatments.
Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics
(2024)
BACKGROUND: This study presents a comprehensive comparison between a marker-based motion capture system (MMC) and a video-based motion capture system (VMC) in the context of kinematic analysis using musculoskeletal models. OBJECTIVE: Focusing on joint angles, the study aimed to evaluate the accuracy of VMC as a viable alternative for biomechanical research.
METHODS: Eighteen healthy subjects performed isolated movements with 17 joint degrees of freedom, and their kinematic data were collected using both an MMC and a VMC setup. The kinematic data were entered into the AnyBody Modelling System, which enables the calculation of joint angles. The mean absolute error (MAE) was calculated to quantify the deviations between the two systems.
RESULTS: The results showed good agreement between VMC and MMC at several joint angles. In particular, the shoulder, hip and knee joints showed small deviations in kinematics with MAE values of 4.8∘, 6.8∘ and 3.5∘, respectively. However, the study revealed problems in tracking hand and elbow movements, resulting in higher MAE values of 13.7∘ and 27.7∘. Deviations were also higher for head and thoracic movements.
CONCLUSION: Overall, VMC showed promising results for lower body and shoulder kinematics. However, the tracking of the wrist and pelvis still needs to be refined. The research results provide a basis for further investigations that promote the fusion of VMC and musculoskeletal models.
Pelvic floor dysfunction is a common problem in women and has a negative impact ontheir quality of life. The aim of this review was to provide a general overview of the current state oftechnology used to assess pelvic floor functionality. It also provides literature research of the phys-iological and anatomical factors that correlate with pelvic floor health. The systematic review wasconducted according to the PRISMA guidelines. PubMed, ScienceDirect, Cochrane Library andIEEE databases were searched for publications on sensor technology for the assessment of pelvicfloor functionality. Anatomical and physiological parameters were identified through a manualsearch. In the systematic review 115 publications were included. 12 different sensor technologieswere identified. Information on the obtained parameters, sensor position, test activities and subjectcharacteristics were prepared in tabular form from each publication. 16 anatomical and physiologi- cal parameters influencing pelvic floor health were identified in 17 published studies and rankedfor their statistical significance. Taken together, this review could serve as a basis for the develop-ment of novel sensors which could allow for quantifiable prevention and diagnosis, as well as par-ticularized documentation of rehabilitation processes related to pelvic floor dysfunctions.
Pelvic floor dysfunction is a common problem in women and has a negative impact on their quality of life. The aim of this review was to provide a general overview of the current state of technology used to assess pelvic floor functionality. It also provides literature research of the physiological and anatomical factors that correlate with pelvic floor health. This systematic review was conducted according to the PRISMA guidelines. The PubMed, ScienceDirect, Cochrane Library, and IEEE databases were searched for publications on sensor technology for the assessment of pelvic floor functionality. Anatomical and physiological parameters were identified through a manual search. In the systematic review, 114 publications were included. Twelve different sensor technologies were identified. Information on the obtained parameters, sensor position, test activities, and subject characteristics was prepared in tabular form from each publication. A total of 16 anatomical and physiological parameters influencing pelvic floor health were identified in 17 published studies and ranked for their statistical significance. Taken together, this review could serve as a basis for the development of novel sensors which could allow for quantifiable prevention and diagnosis, as well as particularized documentation of rehabilitation processes related to pelvic floor dysfunctions.
Early motion therapy plays an important role for effective long-term healing of joint injuries. In many cases, conventional dynamic orthoses fail to address the intricate movement possibilities of the underlying joints, limited by their simplistic joint representations, often represented by revolute joints, enabling rotations by only one axis. In this paper, a two-dimensional compliant tensegrity joint for use in biomedical applications is investigated. It consists of two compressed members and five compliant tensioned members. Relative movement possibilities are realized by the intrinsic compliance of the structure. In the development of these systems, the first step is the determination of the static stable equilibrium. This analysis is conducted in this paper by considering the potential energy approach or by using the geometric nonlinear finite element method. The mechanical behavior of the structure is assessed with a specific emphasis on its mechanical compliance. The primary objective of this study is the investigation of the influence of structural parameters on the overall stiffness and movability of the structure. The results underscore the significant effect of member parameters on the stiffness and movability of the compliant tensegrity joint, particularly under varying load magnitudes. These findings provide insights for optimizing the joint’s performance, contributing to its potential application in advanced orthotic and exoskeleton devices.
