Open Access[AI-Machine Learning] Optimized Sensorless Human Pose Estimation for a Kpop Dance Application
Author(s) -
Gyeong-Seok Jeong,
Nando de Freitas,
Youngsin Cho,
Chanshaui Han
Publication year - 2020
Publication title -
international journal of innovative science and research technology
Language(s) - English
Resource type - Journals
ISSN - 2456-2165
DOI - 10.38124/ijisrt20aug003
Subject(s) - computer science , convolutional neural network , artificial intelligence , artificial neural network , machine learning , deep learning , pose , dance , range (aeronautics) , engineering , art , literature , aerospace engineering
There has been a great effort to use technology to make exercise more interactive, measurable and gamified. However, in order to optimize the detection accuracy, these efforts have always translated themselves into motion detection with multiple sensors including purpose specific hardware, which results in extra expenses on both the content production and consumption and induces limitations on the final mobility of the user. In this paper we aim to improve the accuracy, learning speed and detail range of Posenet’s AI sensorless human pose detection by using an artificial neural network to optimize its extraction and comparison algorithms, changing the current model that uses a ResNet convolutional neural network (CNN) to a model using DenseNet and developing a new algorithm for detailed corrections using relevant artificial neural networks. The findings here will be applied on a posture correction system for a dance and fitness application.