# Hand Gesture Recognition on Edge Devices:
## 14 Conclusions and Outlook
Therefore, the algorithm has to evaluate the received echo severally for different beam directions to analyze an area of similar size. Beamforming on the receiver side requires simultaneous processing of several directions. This entails the use of significant processing power but increases processing speed. Band-pass filtering enhances the signal in the regarded frequency range and removes unwanted frequencies from the signal. This can distort the signal but has mostly a strongly positive effect and requires little processing power. The classification methodology highly depends on pre-processing. The lower the amount of pre-processing, the larger the model size. Novel processes for ML enhance robustness, facilitate learning, and lower the amount of training data needed. Table 3 compares classification approaches, including algorithms and processes for HGR. Independent of the sensor, HGR systems have some common goals, such as increasing \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Advantages** & **Drawback** \\ \hline No pre-processing algorithm & No information lost & No compression \\ \hline \multirow{4}{*}{Tgc} & \multirow{4}{*}{Negates effect of attenuation} & Difficult to know required strength, adaptability to current situation only with calibration possible, may enhance far away objects too much - cause of multiple reflections \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline Hilbert envelope & Makes data more clear & May cause data loss \\ \hline \multirow{4}{*}{Cross-correlation} & \multirow{4}{*}{Supports finding the middle of an echo, enhances SNR} & May not be sufficiently accurate (high output where no real echo)/ may cause output of wrong echo \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{4}{*}{Wavelet transform (may include frequency-domain components)} & \multirow{4}{*}{Supports finding the middle of an echo, enhances SNR, more flexible than cross-correlation} & May not be sufficiently accurate (high output where no real echo)/ may cause output of wrong echo \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{4}{*}{Intensity map} & \multirow{4}{*}{Good visualization possible, can be used in combination with image processing} & Little data compression \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & Less granular and precise than CSI \\ \hline \multirow{4}{*}{CSI} & \multirow{4}{*}{Information about the channel state concerning scattering, fading, and power decay; channel state contains information about the environment, objects and changes of the environment} & \multirow{4}{*}{More difficult to obtain than RSS} \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Advantages and disadvantages of different pre-processing algorithms in the time domain for HGR. the robustness of the system, ease of use, and ease of integration in an overall system or in an edge device. Usually, one of them is the main goal targeted by a novel solution presented in a paper. A summary of main goals and possible solutions for further HGR research is given in Table 15. Most opportunities for improvement are seen in the robustness of touchless HGR systems. This includes making the system usable in different environments. Retraining and calibration may be a way to go. At the same time, these are feasible approaches to mitigate the impact of concept drift. The best case is generalizing the system sufficiently such that it works independent of surroundings, sensor, and user.