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Fatigue Estimation Using Peak Features from PPG Signals

Mathematics 2023, 11(16), 3580; https://doi.org/10.3390/math11163580

by Yi-Xiang Chen¹, Chin-Kun Tseng^2,3, Jung-Tsung Kuo², Chien-Jen Wang^2,4, Shu-Hung Chao², Lih-Jen Kau², Yuh-Shyan Hwang² and Chun-Ling Lin^1,*

Reviewer 1: Anonymous

Reviewer 2:

Surendiran Balasubramanian

Mathematics 2023, 11(16), 3580; https://doi.org/10.3390/math11163580

Submission received: 31 July 2023 / Revised: 14 August 2023 / Accepted: 16 August 2023 / Published: 18 August 2023

(This article belongs to the Special Issue Advanced Computational Biology and Bioinformatics)

Round 1

Reviewer 1 Report

please see the attachment

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

1. how the table 1 and table 2 values are finalised?

2. what is the model used for prediciton of fatigue is not clear in paper.

3. Overall workflow diagram is missing.

4. How do you evaluate the model? what metrics used? which model used for prediction?

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

All major comments were adequately addressed and the Authors have done an admirable job of improving the quality of the manuscript. Therefore, it can be accepted without any structural modification.

Reviewer 2 Report

author made changes based on review comments

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Fatigue Estimation Using Peak Features from PPG Signals

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