# Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
## 5 Conclusions and Discussion  
In this paper we have presented an approach that aims to recognize a set of simple gestures in real-time. We proposed a novel set of features extracted from the 3D positions of skeletal joints and evaluated them using several well-known machine learning approaches. We also introduced a dataset comprising of RGB, depth and skeletal data for several gestures. Our approach was evaluated based on the assumptions that a gesture-driven NUI may be used by (a) a limited set of known users, i.e., both training and testing sets will be built using gesture examples from all users; and (b) unknown (previously unseen) users, i.e., training and testing sets will not have common users. The experimental results indicated that our approach, when using ET for classification, was able to achieve more than satisfactory results, outperforming state-of-the-art methods. Moreover, we investigated the effect of mischievous users, i.e., users which (intentionally or not) fail to successfully imitate the exemplar gestures. We showed that upon the sanitation of the dataset from such users, we were able to achieve significant accuracy improvement. Finally, we performed a comparison of prediction times of trained classifiers under different CPU architectures. In Table 9 we summarize all related work of Section 2. As it can be observed, the majority of included research works do not propose a set of features on joints; rather they use joint coordinates and in many cases heuristics, which limit their capability to expand to other gestures. Also, performed evaluation in most cases is poor, most times limited to 3-5 subjects. High accuracy rates are achieved using custom and not publicly available datasets. On the other hand we proposed a scalable approach which uses features, does not rely on heuristics and is evaluated using a significantly larger set of users. Future work will focus on expanding the set of gestures, by adding more complex ones and more samples of the existing ones. We also aim to investigate more machine learning approaches and extend our data set with more users. We also aim to use the approach in the context of the smart meeting room [37] developed within the IoT ecosystem of [38]. Therein it may act as an alternative means of controlling devices in the room, e.g., HVAC, lights, projector etc., and will be transformed into an exposed web service. Of course, in such a scenario, there will be the need for temporal segmentation or continuous recognition of performed arm gestures. Such a segmentation may be performed e.g., by finding the statit position of the actor [39], by applying sliding window volumes comprising of consecutive video frames [40], or even in a totally unsupervised manner by using clustering and keyframe extraction [41]. We should note that one way to avoid the aforementioned computationally expensive task of temporal segmentation, the initialization of the gesture recognition may be triggered e.g., with a predefined pose, which shall also act as a means for the selection of the device to be controlled. We plan to perform both quantitative and qualitative evaluations, i.e., also assess user experience when using such an approach within a smart environment. Finally, upon the completion of the aforementioned functionalities we plan an evaluation by real-life users. Among the issues we plan to investigate is the effectiveness of the proposed approach with users that cannot successfully perform (some of) the predefined gestures, i.e., people with limitations, disabilities or diseases such as Parkinson's. \begin{table}
\begin{tabular}{c c c} \hline \hline  & [18] & **Our** \\ \hline Acc. (\%) & 91.0 & **96.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison to state-of-the-art research work of [18], using our own dataset.