Sport Analysis |

Correct Execution of Weightlifting Exercises using Pose Estimation

Wed, 11 Jan 2023 00:00:00 +0000

Abstract

This thesis is a technical analysis of the weightlifting exercise deadlift, using pose detection and machine learning. Weightlifting is an increasingly popular exercise method with substantial benefits. However, if done incorrectly could lead to injuries. The project aims to research whether machine learning technologies help create a solution that is an alternative to hiring a personal trainer. The technical goal is to recognize correct and incorrect movements from video input by running videos through Google MediaPipe pose detection to gather x, y, and z coordinates. The dataset contains either correct or incorrect video labels to feed the machine learning prediction model to predict whether or not a particular video was correct or incorrect. By doing this, the trained model can clear distinct between correct and incorrect movement, resulting in an alternative to hiring a personal trainer.

Correct Disc Golf Form: Classification of the Backhand Throw using Neural Networks

Fri, 24 Jun 2022 00:00:00 +0000

Abstract

Form is essential when analyzing and reviewing a backhand disc golf throw. The form defines if the throw is performed correctly and the poses of the body define the form. By looking at the body poses the throw can be classified, critiqued, and improved upon. The form consists of different motions which are analyzed using 3D data collected using machine learning solutions on a data set of recorded disc golf throws. By processing the 3D data from recorded throws the form is classified into three classes that represent the start, mid, and end of the throw. The three classes are shown as clusters using Principal Component Analysis (PCA). The PCA showed more overlapping clusters for the start and middle of the throw compared to the end. Classification solutions include a variation of trained LSTM networks and a solution using MediaPipe Pose Classification. The paper concludes that LSTM models perform faster and more accurately than the solution using MediaPipe Pose Classification when analyzing disc golf throws. However, the classification only provides insight for classifying the different forms and not the quality of form.

Depth Compensation Model for Gaze Estimation in Sport Analysis

Tue, 15 Dec 2015 00:00:00 +0000