# Hand Gesture Recognition on Edge Devices:
## 10 Evaluation of HGR According to Focus Areas
### Other Innovative Solutions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Classification** & **Data Type** & **Accuracy** & **Real-Time System** & **Continuous Learning** & **Stability in Different Environments** \\ \hline Tree-ensemble [47] & Ultrasound & 96.7\% & Yes & Incremental training & Cross-user \\ \hline TCN, beamforming [44] & Ultrasound & 92–100\% & Yes & Compound gestures & - \\ \hline Convolution based, training enhanced with unsupervised learning [66] & \multirow{3}{*}{Radar} & \multirow{3}{*}{96.72\%} & \multirow{3}{*}{No} & Possible with new unsupervised data & 6 different indoor locations \\ \cline{3-5}
3D CNN, meta-learning [96] & & & & & possible \\ \cline{3-5}
3D & & & & & \\ \hline Similarity measure [98] & Ultrasound & Only one sample for training: 93.97\% & No & & \\ \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of algorithms used for HGR with continuous learning. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Data Type** & **Accuracy** & **Real-Time System** & **Stability in Different Environments** \\ \hline Convolution-based feature extraction(SonicGestestNet: convolutions and 2 residual blocks as 3 suggested in ResNet), GAN with Wasserstein distance based on gradient penalty for example generation [65] & Ultrasound & 98.9\% & No & 2 indoor environments \\ \hline Based on signal change primitives, thresholds [34] & WiFi & 87.5\% with one access point, 96\% with 3 access points & No & 2 indoor environments \\ \hline SVM [100] & WiFi & 88–92\% depending on scenario (non line of sight, line of sight respectively) & No & 2 indoor environments \\ \hline Wavelet analysis and dynamic time warping [101] & WiFi & 97.3–91.8\% & No & 5 indoor scenarios \\ \hline Phase difference matrix, modified attention-based bi-directional gate recurrent unit classification, space recovery for automated feature extraction [102] & WiFi & 96\% & No & 96\% in two indoor environments \\ \hline Time reversal resonating strength, angle and intersections b/w segments [103] & WiFi & 92\% & No & \begin{tabular}{c} “Certain” amount of environmental shift \\ \end{tabular} \\ \hline CNN, transformer encoder [62] & WiFi & \begin{tabular}{c} In domain: 96.78\%, cross domain: 88.27\% \\ \end{tabular} & No & \begin{tabular}{c} Cross domain means \\ cross-user in this paper \\ \end{tabular} \\ \hline Attention based [104] & WiFi & \begin{tabular}{c} In domain: 99.69\%, cross-location: 96.95\%, cross-orientation: 93.71\% \\ \end{tabular} & No & \begin{tabular}{c} In domain, cross-location, cross-orientation tests \\ \end{tabular} \\ \hline AlexNet (convolution & & & & Stable for different time per gesture; difficulties with different phone placement and distance \\ learning) [105] & Ultrasound & 92.9\% & No & \begin{tabular}{c} Sensitive to \\ environmental changes \\ like wind and angles \\ \end{tabular} \\ \hline Traditional signal processing for 1D tracking [83] & Ultrasound & up to 2.5 mm & Yes & \begin{tabular}{c} Sensitive to \\ environmental changes \\ like wind and angles \\ \end{tabular} \\ \hline Principal component analysis [106] & WiFi & 93\% & No & \begin{tabular}{c} Two indoor \\ environments tested \\ \end{tabular} \\ \hline K-nearest neighbor, SVM [107] & Ultrasound and audio & \
\begin{tabular}{c} 77.8\% for combination \\ of audio and \\ ultrasound \\ \end{tabular} & No &
\begin{tabular}{c} Cross user accuracy \\ drop even in same \\ environment \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._