# Hand Gesture Recognition on Edge Devices:
## 14 Conclusions and Outlook  
For WiFi, radar, and ultrasound, the sent pulse-frequency modulation can increase SNR and facilitate echo extraction. Signal strength and intensity distribution can be used for evaluation on the receiver side. Following vision, radar is the most researched sensor type for HGR. To allow 3D gestures for vision-based systems, a depth camera or stereo vision technology is required to enhance images with depth information. The goal of pre-processing is to lower the size of the input for classification. This requires reduction of noise and data that does not contribute to classification while keeping useful parts. This increases information density in the data. A possible way of removing noise and unnecessary data is using given information about the physics of hand gestures such as the expected size of the hand, speed, and location of the hand. These properties are the same for all sensor types. Table 13 summarizes advantages and drawbacks of pre-processing algorithms in the time domain. Time domain-based algorithms avoid information loss and time delay through the Fourier transform. It is possible to apply no pre-processing to the input information. This ensures that no data loss occurs before processing. However, the complexity and size of neural networks depend on the size and complexity of the input data. Thus, in the case of no pre-processing, the neural network is more complex and requires more training data and time. Creating an intensity map does not shrink the amount of input data significantly. However, the majority of neural networks for image data evaluation can be used on intensity maps. TGC is a simple signal-processing method applicable in combination with other pre-processing algorithms. It assures that echoes from farther-away objects are amplified. In theory, it thereby enhances all following processing steps. Sometimes, it is not clear how much a signal is attenuated along its path, as that depends on environmental influences. Therefore, it can be difficult to apply TGC correctly, and applying it wrongly can lower data quality. Cross-correlation and wavelet transform both enhance signal parts that look similar to an input signal. The main drawback of such pre-processing is that noise that is similar to the input signal is also enhanced and can cause misinterpretations in the classification algorithm. RSS and CSI reveal information about the status of a transmitted signal and its strength. CSI is more recent and difficult to process but contains more granular and precise information. Those techniques are originally used in WiFi processing but can be adapted for other signal types. Table 14 outlines advantages and drawbacks of pre-processing algorithms in the frequency domain. The majority of pre-processing algorithms do Fourier transform. Often, pre-processing with and without Fourier transform are combined. In radar, usually one or several of the maps RAM, RDM, or ADM are used. Each of them requires quite some processing, and the result is not easily understandable. However, visualizations of those maps work well with image processing algorithms. If it is clear beforehand which information, range, frequency shift, or angle is the most relevant one, processing can be done only on the corresponding map. More fine-grained evaluation is often based on micro-Doppler/spectrogram maps. When processing any type of maps, we face the challenge that gestures are carried out at different speeds. Possibilities to cope with that are dynamic speed or time warping. However, these require substantial processing power. Sending a specific frequency pattern and searching for it in the received data, for example with wavelet transform or cross-correlation, can avoid unintentional enhancement of noise and false echo detections. Beamforming on the sender side increases the SNR as it decreases the width of the sent beam and increases its intensity. However, sender-side beamforming evaluates a lower area at a time.