# Hand Gesture Recognition on Edge Devices:
## 7 Comparison of Algorithms for HGR  
A wide variety of algorithms are useful for HGR. Convolutions, gate mechanisms, tree structures, or transformer structures are the foundations of the algorithms. Some use pre-trained deep networks that are accessible online. Implemented machine learning techniques include meta-, few-shot-, ensemble-, and semi-supervised learning. Supervised learning was used in the majority of cases. Nevertheless, Refs. [39; 54] demonstrate, that signal processing without ML and unsupervised algorithms can be successful. The presented solutions have different focus. Solutions with a completely new approach usually show a prove of concept that HGR is generally possible with their novel setup. The approach's advancement can either be on the algorithm side, on the hardware side, or a combination of both. The improvement is either focused on reducing required resources like power, processing steps, and time as in [20] or on increasing accuracy or robustness of the system, as in [56]. The drawbacks of ML algorithms are requirement of a huge, versatile data set for training and being dependent on the setup of the system. Sometimes even on the specific sensor hardware and environment used. Un-supervised training with new data collected while the system is already in use can be a solution to this problem. If this is \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Modality** & **Advantage** & \multicolumn{2}{c}{**Drawback**} \\ \hline \multirow{7}{*}{Vision} & \multirow{7}{*}{\(\bullet\)} & \multirow{7}{*}{Fine grained} & \(\bullet\) & No depth information \\  & & & \(\bullet\) & Good lighting conditions required \\  & & & \(\bullet\) & High data size \\  & & \(\bullet\) & Lots of information & \(\bullet\) & High power consumption \\  & & & \(\bullet\) & High processing requirements \\  & & & \(\bullet\) & Big sensor size \\  & & & \(\bullet\) & Privacy issues \\ \hline \multirow{3}{*}{Radar} & \(\bullet\) & Low power consumption & \multirow{3}{*}{Less accuracy} \\  & \(\bullet\) & Smaller sensor size & \(\bullet\) & Susceptible to electro-magnetic noise \\  & & \(\bullet\) & Works in darkness & \\ \hline \multirow{3}{*}{Ultra-sound} & \(\bullet\) & Lowest power consumption & \multirow{3}{*}{Low range} \\  & \(\bullet\) & Smallest sensor size & \(\bullet\) & Low resolution \\  & \(\bullet\) & Works in darkness & \(\bullet\) & Susceptible to acoustic noise \\  & & \(\bullet\) & Low processing power needed & \\ \hline \multirow{3}{*}{WIFI, LTE, 4G, 5G} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{Hardware easily available} & \(\bullet\) & CSI or RSSI pre-processing required \\  & & & \(\bullet\) & Very low accuracy \\  & & & \(\bullet\) & High noise in most surroundings \\ \cline{1-1}  & & \(\bullet\) & Scalable & \(\bullet\) & Often cannot work simultaneously with normal \\ \cline{1-1}  & & & & WIFI use for connection to the internet \\ \cline{1-1}  & & & \(\bullet\) & Higher power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of advantages and drawbacks of different sensor modalities. done automatically it is called continuous learning. Furthermore, this allows to adapt the system to the current user, setup, and surrounding, improving robustness and accuracy. Zero-, one-, and few-shot learning allows users to add new gestures on the go. Since the early days of convolution based neural networks in the 90s, starting with the introduction of LeNet in 1998 for digit recognition in images, this network type kept evolving and is nowadays used for many tasks including HGR. It soon became obvious that a trade off between network depth and efficient processing must be found as [57; 58] show. The algorithm presented in [37] relies on a circular arrangement of one ultrasonic sender and eight receivers whose data is being evaluated with a combination of CNN and LSTM. Input features are either depth or intensity, or a combination of both. CNN accounts for space and LSTM for time evolution of the gestures.