## 5 Conclusions  
Throughout this paper, we proposed an approach to upper arm gym-workout classification problem according to the spatio-temporal features of sEMG and joint kinematic data. First, in our approach, two upper arm gym-workouts--dumbbell curl (target muscle: biceps brachii) and dumbbell kickback (target muscle: triceps brachii), respectively--were demonstrated by a professional trainer. During the demonstration, sEMG data sample and elbow joint angle data sample were measured and stored by Myo armband and Kinect v2 at every 20 ms, respectively. Next, after RMS filtering and integrating, the processed both data were merged into and visualized as one image with considering the level of manipulation for the recognizability of input image features from human eyes' point of view. Finally, CNN (AlexNet) was employed to impute a relationship between muscle activation and joint kinematics by being trained with the visualized image data set as well as to solve the gym-workout classification problem.  
The statistical result on CNN performance metrics showed that our approach--controlling the level of manipulation for the recognizability--had a significant main effect on CNN performance; of course, so did the level of data augmentation. We also found that there were the interaction effects of two control factors, which should be considered to find the optimal combination of the level of data augmentation and the level of manipulation for the recognizability of input image features as design parameter. Pairwise comparison did not reveal any significant difference when an input image data was visualized as \(\mathcal{I}_{\text{easy}}\) or \(\mathcal{I}_{\text{fair}}\). However, both visualization approaches showed the outperformance over \(\mathcal{I}_{\text{chal}}\).  
The contribution and innovation by disseminating our findings can be summarized as follows. First, this study proposes a novel approach to approximate a relationship between muscle physiology and joint kinematics via CNN feature extraction. Second, by providing a systemic procedure as well as a quantitative analysis, this study advances the state of the art in the problem of developing exercise monitoring systems. Finally, the outcomes related to the level of AAFT and the visualization technique can be utilized to determine design parameters when we develop the exercise monitoring system which is compact and cost-affordable.  
Future studies should be followed in the directions of increasing the number of gymn-workouts to be classified as well as investigating the sparsity and the entropy of the input image data according to the level of manipulation for the recognizability. Based on findings in this study, the proposed approach will serve as a core and be culminated to the development of an exercise monitoring system by which trainee's muscular physiology as well as joint kinematics are properly monitored.  
Conceptualization, J.-H.Y. and H.-U.Y.; methodology, J.-H.Y. and H.-U.Y.; software, J.-H.Y., H.-J.J., Y.-S.J., Y.-B.K., C.-J.L., S.-T.S. and H.-U.Y.; validation, H.-J.J. and H.-U.Y.; formal analysis, J.-H.Y., H.-J.J., C.-J.L., S.-T.S. and H.-U.Y.; investigation, H.-U.Y.; resources, J.-H.Y., H.-J.J., C.-J.L. and H.-U.Y.; data curation, J.-H.Y., H.-J.J., Y.-S.J., Y.-B.K. and H.-U.Y.; writing--original draft preparation, J.-H.Y., H.-J.J., [PERSON]-[PERSON], [PERSON]-[PERSON], [PERSON]-J.L. and H.-U.Y.; writing--review and editing, [PERSON]-H.Y., [PERSON]-J.J., [PERSON]-T.S. and H.-U.Y.; visualization, [PERSON]-H.Y., [PERSON]-J.J. and H.-U.Y.; supervision, H.-U.Y.; project administration, H.-U.Y; funding acquisition, H.-U.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT, Korea, under the National Program for \"Excellence in SW (Grant Number: 2019-0-01219)\" supervised by the Institute of Information and Communications Technology Planning and evaluation (IITP).  
The authors declare no conflict of interest.