Predicting the ground reaction force (GRF) and ground reaction moment (GRM) with a biomechanical model-based approach has an advantage for biomechanical gait analysis in situations where a statistical model cannot be used due to a lack of training data. However, the current prediction methods using a biomechanical model have some issues for clinical application. The present study developed a new inertial measurement unit (IMU)-based method to predict the GRF, the GRM, and the joint kinematics and kinetics with a 3D biomechanical model and simple system. The present method predicts them using a 3D forward dynamics model that computationally generates human movements that minimize the hybrid cost function defined by physical loads and errors between the motion of the model and that of the participants recorded by six IMUs, which allows the prediction system to use only a relatively small number of IMUs. We investigated the prediction accuracy during walking by comparing the new method with a conventional analysis using a force plate and motion capture system. As a result, we observed strong and excellent correlations between the prediction and measurement of the anterior GRF, vertical GRF, sagittal GRM, hip flexion angle, knee flexion angle, hip flexion torque, and ankle dorsiflexion torque. Considering the accuracy of previous studies and that required for gait analysis, the present method could predict them with practical accuracy due to estimated biomechanically valid motions based on optimization using the hybrid cost function that includes a biomechanical evaluation. Moreover, the prediction system has an advantage for clinical applications because the present method observed practical accuracy that has the potential to be applied to some sports analysis and can analyze 3D motion with a simple system consisting of a small number of hardware components and a software.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.

A passive lower-limb assistive device that supports standing work has been developed to reduce lower limb loads in workers. The device effectively prevents lower limb musculoskeletal disorders such as osteoarthritis and contributes to the improvement of occupational health. However, previous studies have not clarified lumbar load when wearing the device. The present study investigated the effect of a passive lower-limb assistive device on lumbar load and verified the effectiveness of biomechanical analysis for evaluating the physical load while wearing the device. Twelve healthy male participants performed an assembly task under three conditions: standing and high and low sitting while wearing the device. We mainly analyzed the joint torque and joint compression force (JCF) computed using a full-body musculoskeletal model based on measurements obtained from a motion capture system and force platform. The present study concluded that the lumbar load increased when wearing the device because the lumbar joint torque was significantly larger than that for standing. In addition, the biomechanical analysis has the advantage of evaluating the lumbar load when wearing the device because the current method of analyzing muscular activity is not able to evaluate the lumbar load including the effects of passive resistance, such as those generated by ligament force and abdominal pressure. Furthermore, the present method is able to simultaneously investigate physical loads in the lower limb and neck joint based on the analysis of the JCFs in the lower limb and the neck joint torque, which is effective as an analytical method for the effect of the passive lower-limb assistive device on the risk of musculoskeletal disorders.

To prevent falls in the elderly, it is essential to evaluate their gait stability and identify factors that negatively affect it. Although one of the probable factors is a decrease in propulsive force of walking, the relationship between the force and the gait stability has not been fully clarified. To this end, two simple walking models were used to investigate the relationship between the propulsive force and the number of steps required to stop, denoted N. N was calculated as the number of steps required for the rimless wheel to stop and was treated as a variable which is an indirect indicator of stability. A lower N corresponds to the gait being closer to a stopped state. The propulsive force was calculated using the push-off impulse applied to the simplest walking model during the step-to-step transition. To account for the effects of the double support phase in human walking, the gravitational impulse, which is the integral of the body weight (gravitational force) over the double support time, was applied to the step-to-step transition equation of the models. The models revealed that the propulsive force is reduced by two factors: the reduction in step length and the reduction in walking speed. In the former, N increases; in the latter, N decreases. The former is consistent with previous experimental results on human gait, whereas the latter has not been experimentally investigated. These results may provide important insights in clarifying the relationship between the stability and the propulsive force in human gait.

The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), one of the components constituting jumps, through a biomechanical model-based approach enables analysis even in environments where force plates or machine learning training data cannot be utilized. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs) and estimated GRFs and body loads using a 3D forward dynamics model. Our estimation method, based on forward dynamics and optimization calculations, generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of the dynamic acceleration in the optimization calculation, the 3D orientation using sensor fusion composed of acceleration and angular velocity data obtained from IMUs and an extended Kalman filter was estimated. As a result, by generating movements based on the cost function, it was possible to calculate biomechanically valid GRFs while following the measured movements even if not all joints are covered by IMUs. This estimation method allows for 3D motion analysis independent of measurement conditions or training data.