# Hand Gesture Recognition on Edge Devices:
## 7 Comparison of Algorithms for HGR
In [59] a ten layer CNN is implemented for HGR with radar micro-doppler maps as input. The authors of [60] extract changes in ultrasound CSI and create a tensor containing space, time, and number of receiver microphone to use it as network input for a CNN model. Ref. [22] introduces the use of meta-learning for HGR. For meta-learning two models are trained. One is designed to help in the learning process of the other. The first one is trained to discover for example the best initialization values for the hyper-parameters of the second one. Utilizing the output of the first one, the second one, which is designed to solve a specific task, requires significantly less training data and time. In the case of [22], the task is HGR. The data is given in three different styles: range-frame-map, doppler-frame-map, and angle-frame-map. Parallel processing of the three input modalities and merging the result gives the highest overall accuracy. Ref. [44] uses a hexagonal arrangement of seven ultrasound transducers. For the processing, beamforming, feature extraction, and a temporal convolutional network are applied to ultrasound spectrograms. The authors of [25] base their predictions on ultrasound data collected with a Samsung Galaxy S10e phone's top and bottom speaker and evaluate hand gestures with Google's Xception model, compare it to a six layered CNN and evaluate late and early fusion. The authors of this research have published their data set. Ref. [21] introduces the use of few-shot learning with one or three support samples for convolution based HGR with micro-doppler radar data. That means, a new, unknown gesture can be performed by the user either one or three times, to make that gesture known to the system which is then able to recognize it. The algorithm consists of an encoder module for automated feature extraction, a comparison model to assess the similarity of the extracted features, and a weighting module that evaluates the similarity measure. The embedding--and the comparison module carry convolutional layers. In [61] all received inputs are initially processed separately with 1D convolutional layers and inception layers. The actual classification uses an LSTM layer. The authors of [62] leverage a combination of deep CNN with the encoder part of transformers and adaboost for ensemble learning on WiFi data. Their main goal is to improve cross domain reliability of the system. The focus of [63] is on edge implementation. Their CNN therefore only consists of feature extraction on each channel, linear transformation, and point convolution, while the main part of the system is used for feature extraction from WiFi data. Ref. [64] found that CNN yielded best results compared with LSTM and decision tree classifiers for gesture recognition on WiFi data. Gestures recognized with WiFi are more coarse than those recognizable with vision, radar, or ultrasound technology and rely on CSI. The following papers deal with convolution based HGR since 2023. Ref. [56] implements a deep CNN on the complex data generated using FFT on MIMO (Multiple-input Multiple-output) radar data. By contrast to the previous approaches the imaginary part of the data is used to increase accuracy. Ref. [65] Generates huge amounts of artificial data using GAN for improved training of a CNN on ultrasound data. Applying a mix of supervisedand unsupervised learning, Ref. [66] enhances HGR's cross-user and cross-environment classification outcomes on radar data. The convolution-based model is trained with labeled and unlabeled data with labels generated by a guessing algorithm. Additionally, it compares pre-processed data from Range-Doppler-, Range-Angle-, Doppler-Angle-Image, and Micro-Doppler Spectrograms, showing that RAI performs best in these comparisons. Ref. [67] employs a combination of convolution and gate based (LSTM) model for HGR of two persons simultaneously with one radar. CNN and TCN are combined in [68] for radar data-based HGR. Ref.