## 5 Discussion
### Estimation Performance of the Proposed Method  
A good system performance is represented by the similarity between the system's output and the actual RMS of the EMG signal. Therefore, a minor error is preferable in the output of the employed machine learning model. According to the GRASP characteristic mentioned in Section 1, the exercise must be challenging for a patient, involving several cylinder sizes and weights. Therefore, a favorable model must demonstrate equally low error across all variations. The results in Figures 13 and 14 showed that all the employed machine learning models achieved an error below the standard deviation of the ground truth. However, Figure 15 illustrates a more comprehensive performance, which depicts the actual estimated RMS of EMG for each model. A good model's output must comprise characteristics or shapes similar to the ground truth, represented by the cyclical pattern in the waveform. Figure 15 shows that all models have a positive correlation coefficient, indicating a correct direction of the estimation. Nevertheless, a higher coefficient number is the most preferable. The MLP model exhibited a waveform shape that was mainly unrecognizable, accompanied by an unstable portion that reached negative values. Accordingly, the MLP model has the lowest correlation coefficient, indicating a great discrepancy with the actual muscle force. The condition of the ground truth was in the normalized form, where the value ranged from 0 to 1. Therefore, a negative value is meaningless and considered a shortcoming of the corresponding model. Subsequently, the SVM model demonstrated better performance with a correlation coefficient of 0.52, as indicated by a waveform shape consisting of an ascending and descending phase, representing the contraction and relaxation of the muscle, respectively. However, the estimated contraction force was identical for all finger conditions, indicating that the SVM model was unable to produce different contraction forces depending on the finger conditions. Additionally, some of the estimated contraction forces exhibited negative values, indicating instability in the estimation performance. This condition is possibly due to the model's inability to properly learn the actual forearm muscle contraction force with the given varied input on the stretch-sensor data. Ultimately, the RF model demonstrated a preferable condition, where no portion exhibited negative values. More importantly, the shape was identical to the actual contraction force, with a correlation coefficient of 0.81, indicating clear estimated relaxation and contraction phases of the muscle. The five peaks of the ground truth corresponding to the first to fifth finger conditions, respectively, show a trend where the first to third finger conditions are increasing, while the fourth to fifth finger conditions are decreasing, accompanied by the relaxation state in between. Accordingly, the RF model exhibited a similar trend in the contraction force for each finger condition, accompanied by relaxation states that did not exceed negative values, indicating stable estimation performance. The RF model demonstrated better performance in similarity to the actual contraction force compared to other models. Thus, the RF model was further utilized to compare it with the actual contraction force of the perceived difficulty of lifting the cylinders. As described in Section 4.2, the ground truth and the estimation showed a similar number of participants for whom the grasping cylinders' contraction force of perceived ease was higher than the perceived difficulty. However, only four participants exhibited a linear relationship between the ground-truth contraction force and the estimation. The first participant had a rather identical contraction force. Meanwhile, the remaining five participants showed a reverse relationship between those data. The possible reason for this condition was the inconsistency in the exhibited contraction force for the corresponding participant.