# Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
## 5 Conclusions and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Approach** & **Gestures** & **Acc.(s.)** & **Comments/Drawbacks** \\ \hline  & & & & heuristic, not scalable rules, different \\
2D projected joint trajectories, rules and HMMs & **Swipe(L,R)**, Circle, **Hand raise**, Push & 95.4 (S) & features for different kinds of sweeps \\ \hline  & & & & \\
1 & 3D joints, rules, SVM/DT & Neutral, T-shape, T-shape tilt/**pointing(L,R)** & 95.0 (S) & uses an exemplar gesture to avoid segmentation \\ \hline  & & & & \\
2 & Norm. 3D joints, Weig, DTW & Push Up, Pull Down, **Swipe** & 96.7 (n/a) & very limited evaluation \\ \hline  & & & & \\
1 & Head/hands detection, GMM & **Up/Down/Left/Stretch(L, R, B)**, Fold(B) & 98.0 (n/a) & relies on head/hands detection \\ \hline  & & & & \\
3 & clustered joints, HMM & Come, Go, Sit, Rise, Wave(L) & 85.0 (2) & very limited evaluation, fails at higher speeds \\ \hline  & & & & \\
1 & HMM, DTW & Circle, Elongation, Push, Swipz(L, R), Smash & 96.0 (4) & limited evaluation \\ \hline  & & & & \\
2 & Differences to reference joint, KNN & **Swipe(L, R, B)**, Push(L, R, B), Clapping in/out & 97.2 (20) & sensitive to temporal misalignments \\ \hline  & & & & \\
3 & 3D joints, velocities, ANN & **Swipe(L,R)**, Push Up(L,R), Wave(L,R)** & 95.6 (n/a) & not scalable for gestures that use both hands \\ \hline  & & & & \\  & & & & \\
4 & Pose sequences, Decision Forests & **Open Arms, Turns User(Proge, Flag, Kaiser/Lower Right Arm)**, & pose modeling requires extra effort, limited to \\
5 & & Good Bye, Jap. Garrecting, **Put Hands Up Front/Laterally** & 91.5 (10) & gestures composed of distinctive key poses \\ \hline  & & & & \\
8 & 3D joints, feature weighted DTW & Jumping, Bending, Clapping, **Greeing**, Noting & 68.0 (10) & detected begin/end of gestures \\ \hline  & & & & & relies on heuristically \\
9 & 3D joints, DTW and KNN & **Swipe(L, R)**, Push Up(L, R, Pull Down(L, R), Wave(L, R) & 89.4 (n/a) & determined parameters \\ \hline  & & & & \\
5 & 4D quaternions, SVM & **Swipe(L, R)**, Clap, Wave(, Draw circle/tick) & 98.9 (S) & limited evaluation \\ \hline  & & & & \\
1 & Head/hands detection, kinematic constraints, GMM & Purch(L, R), Clap, Wave(L, R), Dumbell Curls(L, R) & 93.1 (S) & relies on head/hands detection \\ \hline  & & & & below average performance \\
1 & Motion and HOG features of hands, hierarchical HMMs & **Swipe(L, R)**, Circle, Wave, Point, Palm Forward, Grab & 66.0 (10) & on continuous gestures \\ \hline  & & & & \\
**our** & novel set of features, ET & **Swipe Up/Down/In/Outfit,R)** & 95.0 (10) & scalable, does not use heuristics \\ \hline \end{tabular}
\end{table} 
Table 9: Reported results of the state-of-the-art (alphabetically). The 2 nd column (Approach) summarizes features and learning approach used. In the 3 rd column (Gestures), relevant gestures to those of our work are in bold. The 4 th column (Acc.(s.)) presents the best accuracy achieved in the used dataset and the number of subjects in parentheses. Concluding, we should note that research within the area of gesture recognition has been turned towards deep learning approaches which make use of raw RGB-D data, e.g., as in the work of [PERSON] et al. (2019) and sophisticated deep network architectures. However, although such methods are able to achieve higher accuracy rates, often without the need of hand-crafted features, they typically result to more sophisticated models that need large amounts of data to train. Also such models may not be able to run inference in real time using low-end hardware such as a Raspberry Pi, which is often adopted in several IoT projects. If on the other hand the model runs on a more powerful server the network overhead of the raw data streams may be a bottleneck. Therefore, we feel that hand-crafted features combined with traditional machine learning approaches will be still of use within the forthcoming years for data or computationally constrained applications. Conceptualization, G.P. and E.S. ; data curation, G.P. ; investigation, D.S. ; methodology, E.S. ; supervision, E.S., T.G.