IHP-Leibniz-Institut für Innovative Mikroelektronik, 15236 Frankfurt, Germany.
Institute of Computer Science, Humboldt University of Berlin, Rudower Chaussee 25, 12489 Berlin, Germany.
Sensors (Basel). 2022 Dec 28;23(1):308. doi: 10.3390/s23010308.
Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase.
无线电探测和测距（radar）技术的发展使得手势识别成为可能。在基于热图的手势识别中，特征图像尺寸较大，需要复杂的神经网络来提取信息。机器学习方法通常需要大量数据，而使用雷达采集手势数据既耗时又耗能。因此，提出了一种基于调频连续波（FMCW）雷达和合成手势特征生成器的低计算复杂度手势识别算法。在低计算复杂度算法中，对雷达原始数据进行二维快速傅里叶变换，生成距离多普勒矩阵。然后，应用背景建模将动态目标和静态背景分离。然后在距离多普勒矩阵中选择幅度最大的一个 bin 来定位目标，并获取其距离和速度。在该位置处的天线沿天线维度的 bin 可用于使用傅里叶波束转向计算目标的角度。在合成生成器中，使用 Blender 软件生成不同的手势和轨迹，然后直接从轨迹中提取目标的距离、速度和角度。实验结果表明，当使用合成数据作为训练集，真实数据作为测试集时，该模型在测试集上的平均识别准确率可达 89.13%。这表明合成数据的生成在预训练阶段可以做出有意义的贡献。
