# Hand Gesture Recognition on Edge Devices:
## 8 Efforts to Bring HGR to the Edge
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Classification Algorithm** & **Data Type** & **Accuracy** & **Size** & **Hardware of Online System** & **Inference Time** & **Energy Consumption of Inference** & **Effort for Edge Applicability** \\ \hline ShuffleNetV2 & & & & & & & \\ with GAN generated synthetic data [24] & Sound of finger sliding on surface & 92.3\% & 34 k parameters & Android application on phone & — & — & Specific implementation \\ \hline RNN [20] & Radar & 98.4\% & 120 k RAM, 278 k flash & 32-bit PSoC 6 Arm Cortex-M4 & 33\% CPU time & 75 mW & TensorFlow Lite Micro, 32-bit single precision floating point numbers \\ \hline LSTM [69] & Radar & 99.10\% known participant, 98.48\% new participant & — & Laptop & — & — & Lower sampling frequency, fft size, extraction of motion profiles \\ \hline Traditional signal processing, Doppler effect [53] & Ultrasound & 94.67–94.33\% & — & Laptop & — & & \\ \hline Range-doppler traditional signal processing, Doppler effect [53] & Radar, depth image, sensor fusion & \(\times\), y, z direction error: 13.8, 9.7 mm & — & TI Tiva Cortex M4F microcontroller & 31 ms & \(<\)1 W by the radar—no further info & - \\ \hline \hline \end{tabular}
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Table 6: Comparison of algorithms used for HGR with focus on real-time systems that have actually been tested in real-time. \begin{table}
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Table 5: _Cont._ \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Classification Algorithm** & **Data Type** & **Accuracy** & **Size** & **Hardware of Online System** & **Inference Time** & **Energy Consumption of Inference** & **Effort for Edge Applicability** \\ \hline Range-doppler, convolution based DNN [86] & FMCW Radar, RGB, depth image, sensor fusion fusion & 94.1 (leave one version out validation) & **—** & Quadro 6000 NVIDIA GPU & 52 ms & 1 W for the imaging sensors; imaging sensor only used when gesture detected & **—** \\ \hline Analog signal processing, beamforming, traditional signal processing [39] & Ultrasound & 0.73–1.04\% error & 64.70 kBytes & Low cost MCU & 20–45 Hz & **—** & After ftt only required \(t\)-bins further processed \\ \hline MLP, radial fusion function (Neuroshield), decision tree on IoF [82] & Ultrasound & 92.87\%, 98.4\%, 96.94\% & 23 KB, 136 KB, 273 KB & 23 KB, 136 KB, 273 KB & 7.42–18.3 depending on General Vision, Neuroshield device added & **—** & High compression of data during pre-processing (ToF basis for classification) \\ \hline Optimization-based meta-learning [23] & Radar & 1-shot: 84\% 5-shot: 94.2\% & **—** & On Intel Neural Compute Stick & 55–287 ms & Minimum: 5 mW per radar duty cycle & Two solutions: dimensionality convolution layers for feature extraction \\ \hline Temporal graph kNN and attention based & Data type & 98.71\% parameters & 1.56 RPi4 & 0.4 s (batch size 16) & — & Specific type of NN \\ \hline DPNN [77] & & & & & & AutoTiler tool generated \\ \hline
2D Convolutional neural network, temporal convolutional network [78] & Radar & 86.6\% parameters; 92 KB network [78] & 46,000 ultralow 92 KB & Parallel ultralow power processor & Up to 175 Hz tested & Up to 175 Hz & 21 mW (for real-time prediction), Code for parallel execution of the model \\ \hline Transformer [79] & Radar & 77.15\% & 263 kB & 46,000 ultralow 92 KB & 92.4 ms & 0.47 mJ & **AutoTiler tool generated** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._