低成本惯性传感器的信号增强与手势识别

The low-cost inertial sensors have serious errors. To address the problem, a three-stage signal enhancement strategy is proposed. For the first time, the IMU signal randomness is quantified through using the complex network deterministic index. Then, random errors can be controlled effectively by empirical modal decomposition and wavelet transform. Furthermore, the time-frequency features are fused with the topology features of the complex network in the IMU motion recognition task. For the first time, the motion habits of different users and various types of IMU with different performances are considered, in which case, the recognition accuracy for 62 kinds of 3D gestures is improved to 92.4%. In addition, the value of complex network topological features in motion state recognition and time series analysis is demonstrated by comparing the gesture recognition accuracy of various machine learning and deep learning models.