# Hand Gesture Recognition on Edge Devices:
## 10 Evaluation of HGR According to Focus Areas
### Other Innovative Solutions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Data Type** & **Real-Time System** & **Number of Sensors** & **Resolution** & **Special Feature** \\ \hline Beamforming, peak detection for distance & Ultrasound & Yes & 1 transmitter, 7 receivers & 1.67 cm, 1.5\({}^{\circ}\) & Creates a B-mode image \\ estimation [38] & Ultrasound & Yes & 1 transmitter, 7 receivers & Radial distance & Thin-film aluminum nitride \\ Traditional signal processing [46] & Ultrasound & Yes, 4.4 ms at maximum range & 1 transmitter, 7 receivers & arc error: \(\pm\)1.5 mm, 2, \(\pm\)3.5 mm, \(\pm\)3.5 deg & Transducers, creates a B-mode image \\ \hline Amplitude & & & & \\ envelope, B-Scan & & & & \\ (horizontal & & & \\ sectional image), & & & & \\
3D-Scan (3D volume image) & Ultrasound & Yes & 64 Murata & Lateral resolution: \(11^{\circ}\), axial: \(171.5\) mm & Creates B-Scans \\ visualization with & & & & \\ OpenGL (no & & & \\ HGR) [40] & & & & \\ \hline Traditional signal processing for 1D tracking [83] & Ultrasound & Yes, 2.76 ms per 10.7 ms audio segment & 1 transmitter, 1 receiver & Accuracy up to \(11^{\circ}\), axial: \(11^{\circ}\), axial: \(171.5\) mm & Continuous wave, implementation on a phone \\ \hline Complex-valued neural networks for DOA angle estimation [92] & Radar & No & 4 transmitters, 4 receivers & Mean absolute error less than 4\({}^{\circ}\) & Processing of complex values \\ \hline Multi dimensional dynamic time warping of 60 features found with & Radar & No & 1 transmitter, 4 receivers & 2.7 cm, 2.2 cm/s & Quantum-inspired evolutionary algorithm \\ quantum-inspired evolutionary algorithm [91] & & & & \\ \hline CNN [95] & Ultrasound & No & 1 transmitter, 1 receiver & Doppler f: 15.6 Hz, \(11^{\circ}\), velocity: 9 mm/s, range 6.9 mm, unambiguous & Spectrogram/micro \\  & Ultrasound & No & 1 transmitter, 1 receiver & \(11^{\circ}\), distance: 0.172 m, unambiguous & Doppler based features used for classification \\  & & & & \\  & & & & \\ \hline Channel Impulse Response, convolution based classification [60] & Ultrasound & Yes & 2 speakers, 1 to 4 microphones & 7 mm & Implementation on a phone; impact of number of microphones \\ \hline ToF, beamforming [36] & Ultrasound & Yes, 170 fps & 1 transmitter, 7 receivers & 0.42 mm error at \(0.5\) m distance & Creates a B-mode image \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Data Type** & **Accuracy** & **Real-Time System** & **Stability in Different Environments** \\ \hline \multirow{3}{*}{CNN-Transformer [55]} & \multirow{3}{*}{Radar} & \multirow{3}{*}{Up to 98\%} & \multirow{3}{*}{No} & Interference by a \\  & & & & & cylindrical object and a \\ \cline{3-5}  & & & & non-recognizable hand \\ \cline{3-5}  & & & & & gesture \\ \hline LSTM-CTC & & & & & Robustness test \\ (Connectionist Temporal & & & & regarding distance, \\ Classification) [99] & & & & scale of hand gesture, \\ \cline{3-5}  & & & & duration time of hand \\ \hline LSTM [69] & \multirow{3}{*}{Radar} & 99.10\% known participant, 98.48\% new participant & Yes & 99.10\% known participant, 98.48\% new participant \\ \cline{2-5}  & & & & participant \\ \hline Traditional signal processing, Doppler effect [53] & Ultrasound & 94.67–94.33\% & Yes & Home, Cafe (accuracy respectively) \\ \hline FFT normalization, squared continuous frame subtraction, Gaussian smoothing, Doppler effect [54] & Ultrasound & up to 96 \% & Yes & Tested in 2 indoor environments, in a bus \\ \hline ShuffleNetV2 with GAN generated synthetic data [24] & Sound of finger sliding on surface & & & 3 indoor scenarios \\ \hline Tree-ensemble [47] & Ultrasound & 96.7\% & Yes & Cross-user \\ \hline Fresnel zone calculation, decrease and increase in reflection path length [32] & LTE & 98 \% & No & 3 environments, indoor and outdoor \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of algorithms used for HGR with focus on robustness.