# Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
## 2 Related Work  
During the last few years, many research efforts have focused on the problem of gesture recognition. In this section we present works that attempt to recognize similar gestures, to those that are recognized in this work and focus on those that include (but are not limited to) arm gestures. Traditional machine learning approaches such as artificial neural networks (ANN), support vector machines (SVM), decision trees (DT) or K-nearest neighbor classifiers (KNN) have been widely used. [PERSON] et al. [1] used an exemplar gesture to mark the beginning of the recognition process and avoid temporal segmentation. Their approach used SVMs and DTs on 3D skeletal joint coordinates. [PERSON] et al. [2] selected the spine joint as reference, computed distances from elbows and hands and used them as features. For recognizing gestures, they used KNN classifier. [PERSON] et al. [3] used 3D joints calculated on extracted key-frames of sequences and cascades of ANNs to firstly classify the gesture side (left/right) and then recognize gestures, accordingly. [PERSON] et al. [4] used SVMs to recognize distinctive key poses. They modelled gestures as sequences of key poses and used decision forests to recognize them. [PERSON] et al. [5] used 4D quaternions to describe joints and SVMs to recognize gestures. Since in general, gestures are temporal sequences that vary in speed (e.g., when performed by different users), the dynamic time warping algorithm (DTW) [6] has been exploited in many works, as it is able to tackle precisely this problem. [PERSON] et al. [7] used dynamic time warping (DTW) on 3D points normalized per person. [PERSON] et al. [8] proposed a feature weighted variation of DTW on 3D joints, using the neck as a reference point. [PERSON] et al. [9] presented a similar approach which relies on DTW, KNN classifiers and some heuristics to improve performance. [PERSON] et al. (2019) used 3D joints and compared DTW with an HMM, acquiring the same precision. Hidden Markov models (HMM) and Gaussian Mixture Models (GMM) have also been frequently used, due to their ability to solve temporal pattern recognition problems. [PERSON] et al. (2019) reduced the problem of 3D tracking to 2D, by projecting the tracked joints to a coordinate system attached to the user and aligned to his/her orientation. Their rule-based approach was based on HMM and adaptive thresholds. The technique proposed by [PERSON] and [PERSON] (2019) relied on background subtraction and head/hands detection. The 3D positions of hands and head were used with a GHMM. [PERSON] et al. (2019) used a HMM on clustered sets of 3D joints. [PERSON] and [PERSON] (2019) detected head and hands and then trained GMMs by imposing kinematic constraints in the process. Finally, [PERSON] and [PERSON] (2019) tracked hands and derived feature vectors that represented the hand shape based on HOG and motion features. Then they used a hierarchical model of HMMs for real-time recognition. In Section 5 we summarize the aforementioned approaches, emphasizing on features used, learning algorithm(s) applied, gestures tackled and accuracy achieved on the data set adopted at each. As it will be seen, within this work we evaluate traditional machine learning algorithms, such as SVMs, KNN, HMMs, GMMs and tree models. However, we should herein mention that during the last few years, research efforts have shifted towards deep-learning based approaches. The latter are able to learn features, instead of computing them using an algorithm. [PERSON] et al. (2019) proposed the use of deep spatio-temporal features that are extracted by 3D convolutional neural networks (CNNs) and a model that was built with skeleton information and was based on a long short term memory network (LSTM). [PERSON] and [PERSON] (2019) used two-stream recurrent neural networks, in order to model the temporal dynamics and the spatial configurations of actions. [PERSON] et al. (2019) used a CNN on raw skeletal data, while [PERSON] et al. (2019) used 3D CNNs and convolutional LSTMs.