## 3 Results
### Comparison Result for Various Neural Network Models  
The comparison results for AlexNet and three layer (input-hidden-output) neural networks (NNs) are presented in Tables 4 and 5. h1 through h4 represent the number of hidden nodes in the hidden layer. To evaluate NNs, \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) (which consist of \(9\times 16\) real values) were flattened to \(144\times 1\) vectors, then applied to the NN as an input. Similarly, \(128\times 128\)\(\mathcal{I}_{\text{chal}}\) was converted into \(16{,}384\times 1\) vector. Accordingly, the number of input nodes was set to 144, 144, and 16,384 for \(\mathcal{I}_{\text{easy}}\), \(\mathcal{I}_{\text{fair}}\), and \(\mathcal{I}_{\text{chal}}\), respectively. Since the purpose of this comparison was to find classical NN architectures of which performance was equivalent to that of AlexNet, we increased the number of hidden nodes gradually from 1 to 4. The softmax layer with two nodes was adopted as an output layer as the number of upper arm gym-workouts needed to be classified was two. The rest of the evaluation protocol was the same as introduced in Section 3.1. From Tables 4 and 5, we  
Figure 6: Mean values for (**a**) training loss and (**b**) test accuracy: For (**a**), The training loss shows several tendencies with respect to two factors, but no significant difference found by pairwise comparison. For (**b**), pairwise comparison indeed revealed that the test accuracy of both \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) was significantly higher than that of \(\mathcal{I}_{\text{chal}}\) upto AAFT(4). No significant difference was found between \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) across all AAFT levels. Significance is marked for \(p<0.05\ (*)\). control the level of manipulation for the recognizability of input image features as well as the level of data augmentation by AAFT. We wanted to reveal the effect of those two control factors on CNN training loss and test accuracy by statistical analysis. Furthermore, finding the optimal combination of the two factors for the best CNN performance was a part of our research questions. Our idea of visualizing muscle activation together with joint movement as a heatmap indeed is initiated by approaches to demystify the functional connectivity of a brain across different regions [55; 56]. However, existing research has been reported that it is not so straightforward to define biopotential-kinematic relationship explicitly [57; 58; 59]; hence, we want to count on CNN to impute those relationship implicitly. According to the statistical analysis, the two control factors had significant main effects for both training loss and test accuracy. Therefore, this finding could be the answers to our first and second research questions. Table 1 shows that \(\mathcal{I}_{\text{fair}}\) has a tendency of having a less value (which means better in terms of training loss) than \(\mathcal{I}_{\text{easy}}\) in overall. However, \(\mathcal{I}_{\text{easy}}\) has the least standard deviation than \(\mathcal{I}_{\text{fair}}\) except AAFT(1). This implies that \(\mathcal{I}_{\text{easy}}\) might contribute to the consistency of CNN training. From Table 2, furthermore, \(\mathcal{I}_{\text{easy}}\) shows the best test accuracy compared to the others under AAFT(1) and AAFT(2). This can be interpreted as \(\mathcal{I}_{\text{easy}}\) is the best suitable choice when data augmentation is not considered. The effect of data augmentation becomes dominant as the level of AAFT increases; accordingly, the test accuracy for \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) almost reached to 99% approximately even after AAFT(3). \(\mathcal{I}_{\text{chal}}\) shows the monotonically increasing test accuracy as the level of AAFT increases. Especially, the test accuracy of \(\mathcal{I}_{\text{chal}}\) increases drastically from 76.6 under AAFT(4) to 95.8 under AAFT(5).