);  
[PERSON]  
2 Department of Computer Science, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
2 Department of Computer Science, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. ); [EMAIL_ADDRESS] (H.-J.J.); [EMAIL_ADDRESS] (Y.-S.J. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
2 Department of Computer Science, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. ); [EMAIL_ADDRESS] (H.-J.J.); [EMAIL_ADDRESS] (Y.-S.J. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. ); [EMAIL_ADDRESS] (H.-J.J.); [EMAIL_ADDRESS] (Y.-S.J. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (Y.-B.K. );  
[PERSON]  
2 Department of Computer Science, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
1 Division of Computer and Telecommunication Engineering, Yonsei University, Wonju 26493, Korea; [EMAIL_ADDRESS] (J.-H.Y. );  
[PERSON]  
3  
[PERSON]  
1 Division of Computer and TelecommunicationComputer vision-based approach is one main pillar to build exercise monitoring systems. In the computer vision-based approaches, the exercise monitoring system is typically equipped with RGB-d (or RGB) cameras such as Microsoft Kinect, Intel Realsense, etc. [7; 8]. For the captured video frame or image, people's joint positions are detected and estimated with the support of deep learning-based algorithms, then their posture is displayed as a skeleton [9; 10]. [PERSON] et al. proposed an upper limb strength training system in which a user's posture was detected by Kinect v2 [11]. [PERSON] et al. presented a mobile phone video recording-based approach to realtime indoor workout analysis [12] and a similar approach was reported by [PERSON] and [PERSON] [13]. The aforementioned studies in [1; 2; 3] are of computer vision-based approaches as well; specifically, exercise posture correction for home trainee [1], posture modeling [2], and posture correction during rehabilitation training [3]. Surface electromyography (sEMG)-based approach is the other main pillar which enables to monitor body posture and movements. Due to the inter-subject variability (or difference) of the signal, the sEMG-based approach is used to be supported by advanced neural networks; especially, since sEMG is multichannel time series data, recurrent neural network (RNN), long short-term memory (LSTM), and deep belief network (DBN) are mostly used for desired classification [14; 15; 16]. [PERSON] et al. proposed an approach to classify dynamic hand motions by translating sEMG signals via RNN [17]. [PERSON] et al.