Authors :
G. Jeong; N. Freitas; Y. Cho; C. Han
Volume/Issue :
Volume 5 - 2020, Issue 8 - August
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2EVDZ3w
DOI :
10.38124/IJISRT20AUG003
Abstract :
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.
Keywords :
Sensorless human pose estimation – Artificial Intelligence (A.I.) – Machine learning – Posenet – DenseNet – Posture correction methods – Human motricity – Motion capture methods – Dance – K-pop – E-sports – South Korea.
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.
Keywords :
Sensorless human pose estimation – Artificial Intelligence (A.I.) – Machine learning – Posenet – DenseNet – Posture correction methods – Human motricity – Motion capture methods – Dance – K-pop – E-sports – South Korea.
