A Low-Complexity Air-Digit-Writing Recognition Method Based on Adaptive Trajectory Learning Using MIMO Radar

Touchless hand gesture recognition is one of the hottest areas of the human–computer interaction (HCI). In this article, we propose a low-complexity air-digit-writing recognition method based on adaptive trajectory learning using a frequency modulated continuous wave multiple-input-multiple-output (MIMO) radar. The core idea of the proposed method is the adaptive learning trajectory, which includes the rough trajectory estimation, the effective trajectory interception, and the fine trajectory processing. First, the rough trajectory estimation is sequentially conducted by the clutter suppression, range object detection, direction of arrival (DOA) estimation, angle object detection, and centroid estimation. This could reduce the effective feature dimension and further the complexity of the following network and real-time system. Second, to remove the interference of redundant actions, the effective trajectory interception employs the proposed double adaptive thresholds gradient valley search (DAT-GVS) approach to detect the start and end of the hand gestures. Third, fine trajectory processing is performed by the position-shift, normalization, and resampling to eliminate the differences in hand gestures, including the position, size, and speed, and improve the generalization ability and accuracy. Moreover, in order to further reduce the computational complexity, a single-layer long short-term memory (LSTM) network is constructed for identification. Finally, the method is deployed on a commonly used micro unit control (MCU) to realize real-time system. The experimental results show that the proposed method can achieve an average accuracy of 99.6% in train and test split test, 98.24% in leave one subject out test, 97.4% in real-time test on trained people, and 97% in real-time test on untrained people.