## 4 Results
### Estimation Performance  
In this study, the performance of the employed machine learning models was evaluated by their similarity to the ground truth of RMS of EMG from eight channels, represented by the score of RMSE. Figure 13 shows the distribution of RMSE for each machine-learning model across eight channels and 10 participants. All models showed similar results in that the increase in cylinder weight narrowed the distribution of error. However, heavier cylinders also yielded a higher average of error for all models. Another interesting outcome was obtained, where the SVR and MLP models produced identical error distribution trends across weight variations, whereas the RF model was unique. Furthermore, the RF model achieved an overall lower error than the SVR and MLP models. This finding was further supported by the results of an independent \(t\)-test, which showed that the error distribution  
Figure 12: Data collection flow. of the RF model differed significantly from that of the other models. Meanwhile, the error distribution of the SVR and MLP models was not statistically different. To further evaluate the performance of the employed machine learning models, it is necessary to compare the models' RMSE scores and the standard deviations of the ground truth. A comparison between these two parameters is necessary to further evaluate the performance quality of the models in terms of estimating the mean, as mentioned in Section 3.4. As illustrated in Figure 14, the ground truth's standard deviation in blue color shows an increasing value for heavier cylinder weight. This finding suggests that a heavier cylinder weight yields greater muscle power, resulting in a larger amplitude and higher RMS of EMG deviations between the relaxation and contraction states. The same condition also occurred in the RMSE score of all models with heavier weights. According to Figure 14, all models have a lower RMSE score than the ground truth's standard deviation on each weight variation. In the 250 g cylinder, the reductions for SVM, MLP, and RF were 14.5%, 12.9%, and 30.6%, respectively. In the 500 g cylinder, the reductions were 13.5%, 11.2%, and 30.9%, respectively. In the 750 g cylinder, the reductions were 16.6%, 15.2%, and 34.5%, respectively. In the 1000 g cylinder, the reductions were 17.7%, 16.7%, and 36.7%, respectively. This finding suggests that all models outperform simply estimating the mean of the ground truth. The greater the gap from the ground truth's standard deviation, the better the estimation quality. Thus, it can be inferred that the estimation result of the RF model was more similar to the ground truth than those of other models. Figure 14: Average of ground truth’s std. vs the average RMSE score of models. Figure 13: Boxplot of RMSE score of each machine learning model across eight channels and ten participants. The aforementioned finding was further emphasized by the time series estimation result for each model, as shown in Figure 15. The figure represents the ground truth and estimation waveform of RMS for a channel in a cylinder. The RMS ground truth, in blue, showed five waveform peaks representing the contraction force of each finger condition. From left to right, each belonged to the first finger condition until the fifth, respectively. The ground truth showed the highest peak on the third finger condition, where the middle finger was open while the other fingers grasped the cylinder. The estimation waveform of each model, in red, showed varying results. The estimation result of the SVM model showed an identical height in all contraction states and had slightly negative values. The estimation result of the MLP model yielded more negative values, while the shape of the waveform was rather unrecognizable.