[AI-Machine Learning] Optimized Sensorless Human Heartrate Estimation for a Dance Workout Application


Authors : G. Jeong; N. Freitas

Volume/Issue : Volume 5 - 2020, Issue 8 - August

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3jHnPtT

DOI : 10.38124/IJISRT20AUG002

Over the last decade, there has been a great effort to use technology to make exercise more interactive, measurable and gamified. However, in order to improve the accuracy of the detections and measurements needed, these efforts have always translated themselves into multiple sensors including purpose specific hardware, which results in extra expenses and induces limitations on the final mobility of the user. In this paper we aim to optimize a sensorless system that estimates the real-time user heartrate and performs better than the current wearable technology, for further calorie and other vital indicators calculations. The findings here will be applied on a posture correction system for a dance and fitness application.

Keywords : Sensorless heartrate estimation – Artificial Intelligence (A.I.) – Machine learning – Posenet – DenseNet – Real-time heartrate estimation – Heartrate at a distance– Dance – K-pop – E-sports – South Korea.

CALL FOR PAPERS


Paper Submission Last Date
30 - September - 2021

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe