Kinematic and Physiological Analysis of Medieval Combat Sport Using Motion Analysis, Blood Lactate Measurement, and Heart Rate Monitoring: A Case Study.

Medieval combat sport is a form of mixed martial art in which combatants engage in fighting using offensive and defensive equipment while dressed in full armor. The sport is considered extremely taxing, making it nearly impossible to maintain the same level of performance. However, this form of sport has not been thoroughly analyzed, and its impact on human physical response is largely unknown. To address this gap, the study reported here aimed to introduce and test a procedure for analyzing human physical responses within the framework of the sport. To accomplish this, two experienced combatants were asked to engage in a series of strikes, performed in the form of a set duel simulating a professional fight competition. The kinematic aspect of the procedure was examined using motion analysis with the help of an IMU suit, while the physiological aspect was evaluated based on blood lactate levels and heart rate measurements. Furthermore, an ergometer test conducted in a laboratory setting aimed to determine the lactate threshold. The duel results showed noticeable decreases in the kinematic aspects of the strikes, such as the velocity of impact, and a dramatic rise in physiological aspects, such as heart rate and blood lactate levels. During the duel sets, the blood lactate surpassed the threshold level, and at the end, the heart rate exceeded the maximum age-related level. Practicing medieval combat sport has been shown to impose an extreme physical load on the bodies of combatants, noticeably affecting their performance levels.

Estimating the mechanical cost of transport in human walking with a simple kinematic data-driven mechanical model.

This work utilizes a simplified, streamlined approach to study the mechanical cost of transport in human walking. Utilizing the kinematic motion data of the center of mass, velocities and accelerations are determined using kinematic analysis; the applied force is then obtained using inverse dynamics. We calculate the mechanical cost of transport per step from both synthetic and measured data, using a very simple mechanical model of walking. The approach studied can serve as an informative gait characteristic to monitor rehabilitation in human walking.

State estimator based on an indirect Kalman filter for a hydraulically actuated multibody system.

In multibody system dynamics, the equations of motion are often coupled with systems of other physical nature, such as hydraulics. To infer the real dynamical state of such a coupled multibody system at any instant of time, information fusing techniques, such as state estimators, can be followed. In this procedure, data is combined from the coupled multibody model and the physical sensors installed on the actual machine. This paper proposes a novel state estimator developed by combining a multibody model with an indirect Kalman filter in the framework of hydraulically driven systems. An indirect Kalman filter that utilizes the exact Jacobian matrix of the plant at position and velocity level is extended for hydraulically actuated systems. The structures of the covariance matrices of the plant and measurement noise are also studied. The multibody system, described using a semi-recursive formulation, and the hydraulic subsystem, described using lumped fluid theory, are coupled using a monolithic approach. As a case study, the state estimator is applied to a hydraulically actuated four-bar mechanism. The state estimator considers modeling errors in the force model because of its uncertainty in modeling. The measurements are obtained from a dynamic model which is considered as the ground truth, with an addition of white Gaussian noise to represent the noise properties of the actual sensors. The state estimator uses four sensor configurations with different sampling rates. For the presented case study, the state estimator can accurately estimate the work cycle and hydraulic pressures of the coupled multibody system. The results demonstrate the efficacy of the proposed state estimator.

Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter.

The estimation of the parameters of a simulation model such that the model's behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications.