Feedback-RFC Model to Individualize Heartbeat Standards


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 2, pp. 91-102, Feb. 2017
10.3745/KTSDE.2017.6.2.91,   PDF Download:
Keywords: Smart Fitness, Machine Learning, Feedback
Abstract

Many of the wearable smart fitness devices provide services related to users’ heartbeat rates. These services use fixed standards which have been pre-determined based on statistical data. However, because body conditions significantly differ between individuals, the services applying fixed standards to all individuals are not reliable. This paper proposes the Feedback-RFC model which adapts heartbeat standards to individual users' exercise abilities and also proposes a method to implement the model. This paper also shows the effectiveness of the Feedback-RFC model by collecting heartbeat data from 12 participants and evaluating the model with the data.


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Cite this article
[IEEE Style]
T. Kim, P. Jung, S. Lee, K. Chung, C. Keum, S. Kang, "Feedback-RFC Model to Individualize Heartbeat Standards," KIPS Transactions on Software and Data Engineering, vol. 6, no. 2, pp. 91-102, 2017. DOI: 10.3745/KTSDE.2017.6.2.91.

[ACM Style]
Taehyun Kim, Pilsu Jung, Seonah Lee, Ki-Sook Chung, Changsup Keum, and Sungwon Kang. 2017. Feedback-RFC Model to Individualize Heartbeat Standards. KIPS Transactions on Software and Data Engineering, 6, 2, (2017), 91-102. DOI: 10.3745/KTSDE.2017.6.2.91.