# Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
## 4 Experimental Results
### Comparisons to the State-of-the-Art  
In order to assess the efficiency of the proposed method, we performed a comparison using the MSR action dataset of [35]. This dataset consists of 20 gestures, performed by 10 subjects, each performing each action 2-3 times and captured with a depth sensor similar to the Kinect. We compare against the work of [PERSON] et al. [35], which are the authors of the dataset. We also compare against the aforementioned work of [PERSON] et al. [4], which only reported results on Test III. Results are depicted in Table 5. Based on the proposed evaluation procedure, three tests are performed, namely tests I, II and III, on three different datasets, namely AS1, AS2 and AS3. In tests I and II samples from all subjects are used both in training and testing (1/3-2/3 and 1/2-1/2 splits, respectively), while in test III half subjects are used for training and the remaining for testing. We may observe that our method performs very well using the AS3 dataset, has satisfactory results using AS1 while its performance drops when using AS2. This is due to the complexity of gestures involved in each dataset. AS3 is composed of simpler gestures that are closer to the ones we use in our dataset, while AS2 consists of more complex and nuanced gestures  
Figure 6: Mean accuracy per gesture vs. number of users in training set, without the mischievous user.  
Figure 7: Comparison of average time required for the classification of a sample in several architectures.  
that cannot be consistently and accurately recognized with only the use of aggregate features. Moreover, AS1 lies in the middle. We must also note that our method performs well on Test III, which evaluates performance on unseen users, which is a major issue when designing a NUI. Experiments indicate that our method outperforms the one of [PERSON] et al. (2018) in both AS1 and AS3 and on average. However, the method of [PERSON] et al. achieved better results in tests I and II, mostly due the poorest performance of our approach in AS2. Of course, since in test III evaluation considers only unseen subjects, we feel that the superiority of our method therein compared to both ([PERSON] et al., 2018) and also ([PERSON] et al., 2018) is the most remarkable result of the evaluation with the MSR dataset.  
We also performed a comparison to the publicly available dataset of ([PERSON] et al., 2018), which has also been used for the evaluation of ([PERSON] et al., 2018; [PERSON] et al., 2018). This dataset contains gestures similar to those of our dataset, which are also a lot simpler when compared to those of the MSR dataset. Corresponding results are depicted in Table 6. As it may be seen, our approach showed excellent performance, by recognizing all examples. Finally in Table 7 we compare our system to the results of ([PERSON] et al., 2018). This dataset consisted of gestures similar to ours and it was split in three subsets: RotationDB, RelaxedDB and Rotation/RelaxedDB. For the first subset, users inserted rotational distortion with their positioning in respect to the camera. For the second subset, users performed the gestures in a more relaxed way inserting movement of more joints than a gesture needs (for example scratching ones head while performing a swipe). The third subset contained both types of distortion. We may observe that our system outperforms the state-of-the-art approach of ([PERSON] et al., 2018) for both types of injected distortions. Finally, we performed a comparison using our own dataset and the deep learning methodology of [PERSON] et al. (2018) that uses a Convolutional Neural Network (CNN). Results for this case are provided in Table 8. As it may be seen, the proposed feature-based approach showed accuracy which was slightly increased compared to the