# Hand Gesture Recognition on Edge Devices:
## 11 Latest Developments in HGR  
The latest HGR solutions target enhanced cross-user, cross-device, and cross-environment robustness. To increase robustness, different types of deep learning methods \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification Algorithm** & **Data Type** & **Accuracy** & **Special Feature** \\ \hline \multirow{4}{*}{RDFrame from UWB radar [30]} & \multirow{4}{*}{Radar} & \multirow{4}{*}{96 \%} & Novel framework for UWB radar to RDF; tested NN combinations: 3D CNN-FCNN, 3D CNN-k-NN, 3D-SVM, 2D CNN-LSTM \\ \cline{1-1} \cline{2-4}  & & & 3D CNN-FCNN, 3D CNN-k-NN, 3D-SVM, 2D CNN-LSTM \\ \hline \multicolumn{4}{p{113.8 pt}}{GoogLeNet inspired, convolution based, inception modules [29]} & \multirow{4}{*}{Radar} & \multirow{4}{*}{95\%} & \multirow{4}{*}{Based on GoogleLeNet} \\ \cline{1-1} \cline{2-4}  & & & & \\ \cline{1-1} \cline{2-4}  & & & & \\ \cline{1-1} \cline{2-4}  & & & & \\ \hline \multicolumn{4}{p{113.8 pt}}{Cross-correlation, inverse Fourier Transform [35]} & \multirow{2}{*}{WiFi} & recognition of an “X” of 94 cm & Image reconstruction with WiFi \\ \hline \multicolumn{4}{p{113.8 pt}}{SVM (walking person, two people walking, moving wheeled vehicle) (no HGR) [28]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{76.3–94.2\% depending on architecture and 1- or 5-shot architecture and 1- or 5-shot experiment} & Classification for ground moving targets: single person, two persons, vehicle \\ \hline \multicolumn{4}{p{113.8 pt}}{Google Xception Model, Convolution based [25]} & \multirow{2}{*}{Ultrasound} & \multirow{2}{*}{93.58\%} & Data augmentation, different fusion architectures evaluated \\ \hline \multicolumn{4}{p{113.8 pt}}{Nearest Neighbor, modified Hausdorff distance [27]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{96\%} & Sparcity driven method \\ \hline \multicolumn{4}{p{113.8 pt}}{Meta learning, comparison module, weighting module [21]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{95\% For 3-shot, 5-way} & Embedding module with training to learn to compare \\ \hline \multicolumn{4}{p{113.8 pt}}{CNN, LSTM, desiciton tree [64]} & \multirow{2}{*}{WiFi} & \multirow{2}{*}{97.57\%} & Focus on small scale motions with WiFi \\ \hline \multicolumn{4}{p{113.8 pt}}{Convolution-based [59]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{99\%} & Evaluation of benefit of phase information \\ \hline \multicolumn{4}{p{113.8 pt}}{GRU [71]} & Ultrasound & \multirow{2}{*}{93.5\%} & Use of a thin plate for ultrasound signal generation \\ \hline \multicolumn{4}{p{113.8 pt}}{SNN beamforming, GRU classification or quantized spiking NN [70]} & \multirow{2}{*}{Ultrasound} & \multirow{2}{*}{86 \%} & Spike based beamforming approach \\ \hline \multicolumn{4}{p{113.8 pt}}{3 complex linear layers in relation to range, doppler, and angle extraction with DFT + convolution based classifier [94]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{98.06\%} & Suggestions to replace DFT in pre-processing \\ \hline \multicolumn{4}{p{113.8 pt}}{convolution-based from time-velocity—and time-angle diagram XGBoost and LSTM binary classifiers compared [111]} & \multirow{2}{*}{Radar} & \multirow{2}{*}{95\%} & Focus on micro gestures \\ \hline \multicolumn{4}{p{113.8 pt}}{Attention based [87]} & \multirow{2}{*}{Vision, radar} & \multirow{2}{*}{—} & Fusion of radar and optical data; evaluation of early, intermediate, and late fusion and late and intermediate attention mechanisms \\ \hline \multicolumn{4}{p{113.8 pt}}{Support Vector Machine [93]} & Ultrasound & \multirow{2}{*}{92\%} & Introduction of bio-inspired baffles to ultrasound imaging in air \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Other innovative solutions for HGRare combined. Meta-learning, few-shot learning, ensemble learning and semi-supervised learning are processes being explored. Furthermore, information from several types of pre-processing, RDI, RAI, DAI, and micro-Doppler spectrogram are combined and processed in parallel. Temporal and spatial information are fused to improve robustness of the system.