A fitness training optimization system based on heart rate prediction under different activities.

Journal Article (Journal Article)

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction.

Full Text

Duke Authors

Cited Authors

  • Zhu, Z; Li, H; Xiao, J; Xu, W; Huang, M-C

Published Date

  • September 2022

Published In

Volume / Issue

  • 205 /

Start / End Page

  • 89 - 96

PubMed ID

  • 35750282

Electronic International Standard Serial Number (EISSN)

  • 1095-9130

International Standard Serial Number (ISSN)

  • 1046-2023

Digital Object Identifier (DOI)

  • 10.1016/j.ymeth.2022.06.006

Language

  • eng