The problem of walking robots controlled motion synthesis by the inverse dynamic method is considered. The inverse dynamic method equations are represented by the methods of multibody system dynamics as free bodies motion equations and constraint equations. The variety of constraint equations group are introduced to specify the robot gait, to implement the robot stability conditions and to coordinate specified robot links movement. The key feature of the inverse dynamic method equations in this formulation is the presence of the second derivatives of the system coordinates in the constraint equations expressing the stability conditions that ensure the maintenance of the vertical position by the robot. The determined solution of such equations in general case is impossible due to the uncertainty of the initial conditions for the Lagrange multipliers. An approximate method for solving the inverse dynamic without taking into account the inertial components in the constraint equations that determine the stability of the robot is considered. Constraint equations that determine the coordinate movement of individual robot links and required for unique problem solving based on approximate equations are presented. The implementation of program motion synthesis methods in the control system of the humanoid robot AR-600 is presented. The comparison of theoretical and experimental parameters of controlled motion is performed. It has been established that with the achieved high accuracy of the robot links tracking drives control with an error of several percent, the indicators of the robot's absolute movements, in particular, the angles of roll, yaw and pitch, differ from the programmed by 30-40%. It’s shown that proposed method allows to synthesize robot control in quasistatic mode for different movement types such as moving forward, sideways, walking on stairs, inclinations etc.
Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.
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