## 5 Online Classification Experiment
### Results and Discussions  
#### 5.2.1 Classification of 17 Voluntary Movements  
This classification scenario provides some answers to all of the three research questions. The questions are:  
1. How well can the state-of-the-art classification scheme classify 17 types of movements? 2. Can a consumer grade EMG sensor serve as a modality in myoelectric prosthetic control? 3. How long can the user control the prosthetic hands until the accuracy drops significantly? First, let us answer questions 1 and 2. According to Figure 10, under the condition that training and testing are done on the same day, an overall correct rate of 63.8% across subject can be achieved for classification of 17 motions using the proposed method. Also, depending on the participant and the muscle condition, the system can accomplish a correct rate of 82.9% (participant E) or an average accuracy of up to 76.1% (participant H). Furthermore, the statistical results in Figure 11 suggests that improvement in accuracy may be due to the fact that the user has gained more experience from the training on the previous day. However, since the current work only compares the accuracy between two days, it is still too early to draw a decisive conclusion based on the current results. As an extension to this study, more experiments should be conducted for multiple days. It would be interesting to observe how long this trend will persist and how long can the training effect last. Based on the above results combined with the fact that the user can promptly modify any erroneous actions executed by the prosthetic hand by looking at the actual output, the proposed method is functional and has demonstrated the potential to classify 17 motions using six EMG sensors. Furthermore, the results also suggest that a consumer-grade EMG sensor can serve as a modality in myoelectric prosthetic control and other human-machine interface applications. Figure 10: Average overall correct classification rate of 17 voluntary movements of 12 participants. The red error bars indicate standard deviation. The two numbers on top of the bars indicate highest and lowest overall correct rate. Note that the average correct rate of participant A, B, E and H are from the experiment on day 2 (best classification). Also, the mean overall correct rate indicates the overall correct rate across subjects. Although our results cannot be directly compared with results in other studies due the difference in participants, equipment, experiment conditions, type of movement, etc., the results reported in this paper has demonstrated some advantages over existing studies. We will compare these with some of the works summarized in Table 3.  
The first advantage is that the number of movements involved in this work is much greater than most studies. The work done by [PERSON] et al. [32] is the only study that involves more movement than this work. They report very high accuracy of up to 96% for classification of 19 classes. In their work, 19 electrodes are placed on the surface of the hand to capture the EMG signals responsible for fine motor control of the fingers. However, this electrode configuration is limited to only amputees who still retain a portion of the hand. At the same time, it is also inconvenient for healthy people to wear many electrodes. Works by [PERSON] et al. [21] [PERSON] et al. [33] show that accurate classification of 12 finger motions is possible but they also face the disadvantage of having very large number of electrodes. In contrast, our work involves a smaller number of electrodes on the forearm which contributes to reducing equipment costs and preparation time, hence increases the usability of the system. Another wonderful advantage of using small number of electrodes is that it is easier to  
Figure 11: Comparison of mean correct rates of each classification scenario (17 Movements). (*) represents \(p<0.05\), (**) represents \(p<0.01\) and (***) represents \(p<0.001\).