Intersection of triangles in space based on cutting off segment

The article proposes a new method for finding the triangle-triangle intersection in 3D space, based on the use of computer graphics algorithms -- cutting off segments on the plane when moving and rotating the beginning of the coordinate axes of space. This method is obtained by synthesis of two methods of cutting off segments on the plane -- Cohen-Sutherland algorithm and FC-algorithm. In the proposed method, the problem of triangle-triangle intersection in 3D space is reduced to a simpler and less resource-intensive cut-off problem on the plane. The main feature of the method is the developed scheme of coding the points of the cut-off in relation to the triangle segment plane. This scheme allows you to get rid of a large number of costly calculations. In the article the cases of intersection of triangles at parallelism, intersection and coincidence of planes of triangles are considered. The proposed method can be used in solving the problem of tetrahedron intersection, using the finite element method, as well as in image processing.Comment: Convergent Cognitive Information Technologies. Convergent 2019. Communications in Computer and Information Science, in press, Springer, Cham. http://it-edu.oit.cmc.msu.ru/index.php/convergent/convergent2019 (14 pages, 11 figures

Sweat Loss Estimation Algorithm for Smartwatches

This study presents a newly released algorithm for smartwatches – Sweat loss estimation for running activities. A machine learning model (polynomial Kernel Ridge Regression) is used to estimate the sweat loss in milliliters. A clinical dataset of 748 running tests of 568 people was collected and used for training / validation. The data presents a diversity of factors playing an important role in sweat loss: anthropometric parameters of users, distance, ambient temperature and humidity. The data augmentation technique was implemented. One of the key points of the algorithm is an accelerometer-based model for running distance estimation. The model we developed has a mean absolute percentage error (MAPE) = 7.7% and a coefficient of determination (R2) = 0.95 (at distances in the range of 2–20 km). The performance of the fully automatic sweat loss estimation algorithm provides an average root mean square error (RMSE) = 236 ml; more fundamentally, health-related parameter body weight percentage RMSE (RMSEBWP) = 0.33% and R2 = 0.79. To the best of the authors’ knowledge, the algorithm provides the best performance of any existing solution or described in the literature