# Hand Gesture Recognition on Edge Devices:
## 6 History and Categorization of Existing Approaches
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification Approach** & **Advantages** & **Drawback** & **Special Feature** \\ \hline DTW & Supports different speeds & High computational complexity & Similarity measure for time dependent sequences \\ \hline CNN & Extensively researched, successfully used on pictures & No time evaluation & Kernels, convolutions \\ \hline TCN & Keeps information about time, less memory and training time required compared to LSTM, no exploding, vanishing gradient problem & Harder to adapt to meta and transfer learning & Input size equals output size, causal convolutions: all predictions are only based on past timesteps, dilated convolutions: wider convolution area with same computation cost \\ \hline LSTM, GRU (RNN) & Good for time series data, no exploding or vanishing gradient problem & Big model size, many parameters, long and processing intensive training time & Gates for time related information \\ \hline ViT, CrossViT (transformer) & Good for images and time series data, model size better than for LSTM, GRU, faster and less processing intensive training than GRU, LSTM & Large number of parameters, therefore need a large data set for training, long training time & Attention method for time related information \\ \hline SNN & Small size, fast processing, low processing requirements & More difficult to train & Functionality inspired by neurones \\ \hline Fusion architectures & Higher accuracy & Parallel processing required, higher processing load & Combines advantages of several structures in one \\ \hline Random forest, decision tree (ensemble learning method) & Simple & May not be accurate enough especially for fine-grained features & Combinable \\ \hline Meta-learning (zero-, one-, few-shot) & Lower training time, lower training data set required, for adjustable problems, fast adaptation to new problems & Forgetting of old problems & Adaptability; learning to learn; process of training a model or an algorithm to understand and improve its own learning process \\ \hline Federated learning & Data privacy, decentralized training, heterogeneous data, suitable for edge applications & Reaching good overall accuracy is more difficult compared to other architectures & Training takes place in edge nodes, updates of the central model based on gradients or parameters obtained in edge training \\ \hline GAN & Can be used to enhance the data set & Not appropriate for classification & Generates new data to create or enhance a data set \\ \hline Ensemble learning & Merges predictions from several models & More complicated structure & Can be run on edge nodes \\ \hline Reinforcement learning & Decision making agent, adaptive adjustment of signal processing, filter settings, and threshold levels & Exploration and exploitation ratio difficult with limited data & Adaptability over time \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of classification approaches for HGR. The latest systems rely on combinations of classification algorithms. LSTM and GRU structures are well suited for time series data as hand gestures. Transformer is a structure that allows for time-sensitive processing through positional encoding. Ref. [55] uses a combination of CNN and Transformer to create robust classification in the face of interference from for example an object being thrown in close vicinity. Ref. [20] presents a system that is specifically developed to fit on an Arm Cortex-M4 microcontroller. Five features, magnitude, horizontal and vertical angle, velocity, and distance, are used in this case to classify using an RNN structure.