Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data. As an alternative, IMU-based motion capture systems can be used. These systems allow flexible motion collection without nearly any restriction regarding the environment. However, one limitation with these systems is that there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into musculoskeletal simulation software such as OpenSim. Thus, the objective of this study was to enable the transfer of collected motion data, stored as a BVH file, to OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. By using the concept of virtual markers, the motion saved in the BVH file is transferred to a musculoskeletal model. An experimental study with three participants was conducted to verify our method’s performance. Results show that the present method is capable of (1) transferring body dimensions saved in the BVH file to a generic musculoskeletal model and (2) correctly transferring the motion data saved in the BVH file to a musculoskeletal model in OpenSim 4.4.

Patients who suffer from foot drop have impaired gait pattern functions and a higher risk of stumbling and falling. Therefore, they are usually treated with an assistive device, a so-called ankle–foot orthosis. The support of the orthosis should be in accordance with the motor requirements of the patient and should only be provided when needed, which is referred to as assistance-as-needed. Thus, in this publication, an approach is presented to determine the assistance-as-needed support using musculoskeletal human models. Based on motion capture recordings of multiple subjects performing gaits at different speeds, a parameter study varying the optimal force of a reserve actuator representing the ankle–foot orthosis added in the musculoskeletal simulation is conducted. The results show the dependency of the simulation results on the selected optimal force of the reserve actuator but with a possible identification of the assistance-as-needed support required from the ankle–foot orthosis. The required increase in support due to the increasing severity of foot drop is especially demonstrated with the approach. With this approach, information for the required support of individual subjects can be gathered, which can further be used to derive the design of an ankle–foot orthosis that optimally assists the subjects.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other’s weaknesses.

In musculoskeletal simulations, IMU-based approaches are often compromised by errors such as joint angle drift and offset errors due to calibration inaccuracies. These errors can compromise the accuracy of both kinematic and dynamic outcomes. This study presents a simulation-based investigation that uses synthetic inertial and positional data to systematically assess the potential of integrating spatial reference information into IMU-driven inverse kinematics analyses. Optical motion capture data was captured and error-free kinematic and dynamic data was created based on the optical motion capture data. The error-free data was then used as a reference. Based on this reference data, synthetic orientation and position data was created, incorporating a range of error types and magnitudes (e.g., sensor noise, drift, misalignment). To create the IMU-based analysis results, we calculated relative quaternions based on the orientation data which were then converted into Euler angles. We then conducted a sensitivity analysis to determine the spatial accuracy required in the position data to effectively compensate for typical IMU errors. Across all error types and magnitudes, the multimodal inverse approach (using both synthetic IMU and positional data) yielded significantly more accurate results than solely IMU-based analyses. Specifically, the mean joint angle RMSE decreased by $${63}{\%} (\sim {5}^{\circ })$$ , the mean joint torque RMSE by $${80}{\%} (\sim {17}\,\hbox {Nm})$$ , the mean residual force RMSE by $${25}{\%} (\sim {9}\,\hbox {N})$$ , and the mean residual torque RMSE by $${70}{\%} (\sim {26}\,\hbox {Nm})$$ . Future research will evaluate the effectiveness of the multimodal inverse kinematics approach when applied to real-world measurement data.

Every human body is different, however, current movement analysis does not reflect that, as it heavily relies on generic musculoskeletal models. Usually, these models are scaled to match the participants’ body segment lengths and body weight, but not taking individual body shape into account. This can lead to errors in the estimation of joint forces and torques, which are important to accurately estimate musculoskeletal variables. Thus, we developed a method to estimate body segment inertial parameters based on body hulls acquired via smartphone pictures. From the body hull, we infer the skeletal shape and pose, and then estimate the distribution of bone, lean, and fatty tissues. We then segment the body hull and assign each tissue type a density, which is used to calculate the body segment inertial parameters. To allow for the use of our method with existing data, we also introduce two new generic musculoskeletal models, which are based on the average standing body shapes. Validation using MRI-derived ground-truth models shows that our method creates participant-specific musculoskeletal models that are closer to the MRI-derived ground truth than scaled generic models. Additionally, we performed lab-based gait experiments to evaluate the effect of our method on residual forces and joint moments, where we found that our method leads to a reduction of residual forces of up to 14.9 % and a reduction of metabolic cost of up to 12.8 % when compared to generic musculoskeletal models. Our new generic models show similar joint moment outcomes, but less reduction of residual forces than the personalized models.

In musculoskeletal simulation, individualized joint axes enhance the accuracy and reliability of kinematic and kinetic simulation results. We investigated the correctness and performance of an analytical method for identifying the instantaneous axis of rotation between two bodies based on motion data in OpenSim. The instantaneous center of rotation is the point at which two bodies have the same velocity. The relative linear and angular velocity between the two bodies, as well as their relative position to each another, are required as inputs to calculate it. Using the instantaneous center of rotation, fixed or moving joint centers of rotation can be identified. To prove the general applicability of the method, the instantaneous centers of rotation of a revolute joint of a simple double pendulum model and the hip and knee joint of a more complex musculoskeletal model were investigated. The hip joint is defined as a ball joint. The knee joint is defined as an OpenSim custom joint which describes the motion of the child segment in relation to the parent segment as a function of generalized coordinates. To verify the correctness of the approach in OpenSim, the moving centers of rotation were calculated using synthetic noisefree data. The results were compared to the implementation of the respective joints in the model which act as the ground truth. White Gaussian noise was added to the synthetic data to analyze its effect on the quality of the calculated centers of rotation. We were able to correctly identify the center of rotation of each joint using noisefree data. In the case of noisy data, joint centers of rotation can be determined by applying additional filtering or optimization methods to the calculated instantaneous centers of rotation. Consequently, we are able to determine the center of rotation for arbitrary joints based on noisy synthetic data. This approach is applicable for both fixed and moving centers of rotation which distinguishes it from commonly used methods in the field of biomechanical simulation.