employed deep neural network (DNN) architecture to solve the classification problem of wrist position based on sEMG data [18]. The more dexterous motion is, the more advantageous information sEMG carries; accordingly, similar approaches for the classification of finger, hand and arm movements were introduced in [19; 20; 21]. In addition, the monitored muscle activation as well as generated force can directly serve as an indicator to tell whether the target muscle is stimulated during workouts. The sEMG-based force monitoring approaches can be categorized as follows: neural network (NN)-based approach [22; 23; 24; 25; 26], dynamic-model and software-based approach [27; 28; 29], optimization-based approach [30; 31]. The above introduced approaches, so far now, have solely used either computer-vision or sEMG. In contrast, we know that it would be beneficial to endow exercise monitoring systems with multimodality in a sense of information gathering. [PERSON] et al. proposed a posture monitoring system in which both sEMG and inertial measurement unit (IMU) were employed to estimate human motion [32] and [PERSON] et al. introduced a similar approach [33]. [PERSON] used IMU together with Apple Watch to reconstruct arm posture for upper-body exercises [34]. Several studies utilized both sEMG sensor and computer-vision; for instance, equinus foot treatment system by [PERSON] et al. [35], prosthesis control interface by [PERSON] et al. [36], and rehabilitation video games by [PERSON] et al. [37] and [PERSON] et al. [38], respectively. As aforementioned, \"an individual is performing a proper and exact workout\" means that his or her joints are following a correct posture and target muscles are being stimulated primarily. Even though there have been existing studies, an exercise monitoring approach in perspectives of muscle physiology as well as joint kinematics has not yet been fully considered. Studies have striven for demystifying a relationship between sEMG activation and joint kinematics. [PERSON] et al. and [PERSON] et al. proposed Gaussian-Markov process-based elbow joint angle estimation techniques, respectively [39; 40]. Initiated from [PERSON]'s muscle model, [PERSON] et al. introduced a state space model for elbow joint and [PERSON] et al. presented their works for knee joint [41; 42]. [PERSON] et al. used VICON-based 3D motion capture and sEMG data and reported that a relationship between those two were still fuzzy [43]. As seen in the case of sEMG-based force monitoring above, NN-based approach might be a reasonable solution to estimate elbow joint kinematics from sEMG signals [44; 45; 46; 47]. Namely, due to the characteristics of sEMG signal (including individual differences), a relationship between sEMG activation and joint kinematics not so straightforward and existing findings are still debatable [48; 49]. To address the issues above, we propose a systemic approach to upper arm gymn-workout classification via convolutional neural network (CNN). The proposed approach can be regarded as a subsolution of the larger class of problem in the design of exercise monitoring system with a specific example of upper arm gym-workout. The main idea of our approach is to merge joint kinematic data in-between sEMG channels and visualize as one image which will serve as an input to the CNN. The following research questions represent the motivation for this study:  
* \"If muscle physiologic data is visualized together with kinematic data as a spatio-temporal image, can a relationship between these two data be imputed by the CNN since it consists of an automatic feature extractor followed by a classifier?\"
* \"If the input image is manipulated to show more distinctive spatio-temporal features from a point of view of human eyes, then how this manipulation does affect to the CNN performances in terms of training loss and test accuracy?\"  
The above motivation leads us to the following validation procedure.