title: ÐÐ¾Ð½ÑÑÐ¾Ð»Ñ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿ÑÐ°Ð¶Ð½ÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Controlling the correctness of physical exercises using neural networks
Creators: name: ÐÑÐ¸Ð³Ð¾ÑÐµÐ½ÐºÐ¾, Ð¡ÐµÑÐ³ÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ»Ð¾Ð²ÐµÑÐµÑÐºÐ¾Ð¹ Ð´ÐµÑÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
classification
computer vision
human activity recognition
signal segmentation

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ¾Ð½ÑÑÐ¾Ð»Ñ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿ÑÐ°Ð¶Ð½ÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸Â». Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÑÑÑÐ¸Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ðµ ÑÑÐ´Ð° ÑÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿ÑÐ°Ð¶Ð½ÐµÐ½Ð¸Ð¹ ÐºÐ°Ðº Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½ÑÐµ Ð¸Ð»Ð¸ Ð½ÐµÐ¿ÑÐ°Ð²Ð¸Ð»ÑÐ½ÑÐµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐ¸Ð¼ÐµÑÐ¾Ð² Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð¸ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð½ÑÑ Ð¾ÑÐ¸Ð±Ð¾Ðº. ÐÐ°Ð½Ð½ÑÐµ Ð¾Ð± ÑÐ¿ÑÐ°Ð¶Ð½ÐµÐ½Ð¸ÑÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð² Ð²Ð¸Ð´Ðµ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¹ Ð¸ Ð²Ð¸Ð´ÐµÐ¾, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·ÑÑÑÑÑ Ð² Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÐµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ ÑÑÐ´Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ pose estimation. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ: ÐÐ±Ð·Ð¾Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² pose estimation Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð¡Ð¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ñ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿ÑÐ°Ð¶Ð½ÐµÐ½Ð¸Ð¹. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ½Ð°Ð»Ð¸Ð· ÑÐ¾Ð·Ð´Ð°Ð½Ð½ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð². Ð Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ð½Ð° ÑÐ·ÑÐºÐµ python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº TensorFlow, SciPy Ð¸ CatBoost. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð¾Ð·Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ MediaPipe BlazePose. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¾ ÑÐ¾Ð·Ð´Ð°Ð½Ð¾ 4 ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð´Ð»Ñ: Ð¿Ð»Ð°Ð½ÐºÐ¸, ÑÑÐ¾Ð¹ÐºÐ¸ Ð½Ð° Ð³Ð¾Ð»Ð¾Ð²Ðµ, Ð¿ÑÐ¸ÑÐµÐ´Ð°Ð½Ð¸Ð¹ Ð¸ Ð¾ÑÐ¶Ð¸Ð¼Ð°Ð½Ð¸Ð¹ Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð¾Ñ 94% Ð´Ð¾ 97%. ÐÐ»Ð°Ð½Ð¸ÑÑÐµÑÑÑ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð² Ð² Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ðµ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ.
description: The subject of the graduate qualification work is Â«Controlling the correctness of physical exercises using neural networksÂ». This work focuses on the development of machine learning based algorithms that classify execution of a range of static and dynamic exercises as correct or incorrect based on examples of correct execution and common mistakes. Exercise data is presented in the form of photos and videos, which are converted to multivariate time series data using pose estimation. The research set the following goals: A review of pose estimation and classification methods. Creation of suitable datasets for static and dynamic exercises. Development of machine learning based correctness classification algorithms. Analysis of the created classifiers. The work was conducted using python with the help of libraries such as TensorFlow, SciPy and CatBoost. MediaPipe BlazePose was used as a pose estimation system.The work resulted in 4 classifiers for: plank, headstand, squats, and pushups, with accuracy ranging from 94% to 97%. Further works improving the performance and integrating classifiers into an application are planned.