Mid-air gesture recognition method based on inertial sensor

Abstract

The invention discloses a mid-air gesture recognition method based on an inertial sensor. The method includes the steps that a mid-air gesture signal sequence is extracted aiming at sensing signals acquired by the inertial sensor, then data preprocessing is carried out, then a training sample set, a verification sample set and a test sample are acquired, an LSTM-RNN model is subjected to parameter initialization, the training sample set is used for training the LSTM-RNN model, in the training process, verification samples in the verification sample set are input in the LSTM-RNN model trained in the iteration process, the iteration frequency is controlled according to the recognition error rate of the verification sample set, and a final LSTM-RNN classifier is obtained; finally the test sample is input in the final LSTM-RNN classifier, and a gesture corresponding to the test sample is recognized through the final LSTM-RNN classifier. The method has the advantages of being higher in mid-air gesture recognition precision and accuracy.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and artificial intelligence, in particular to an air gesture recognition method based on an inertial sensor. Background technique [0002] Inertial handwriting recognition based on inertial sensors (accelerometers and gyroscopes) is one of the emerging research frontiers in the computer field in recent years. The inertial sensors built into wearable devices or smart handheld devices on the user's body are used to collect the user's handwriting in the air. Acceleration signals and angular velocity signals, through machine learning and deep learning methods to identify the user's writing content, it is one of the important research contents of wearable computing (Wearable Computing) and ubiquitous computing (Ubiquitous Computing). [0003] At present, air gesture recognition based on inertial sensors has a wide range of applications in smart home, automatic driving, education, medical ca...

AI Technical Summary

Problems solved by technology

Because the signal waveform of the inertial sensor is very abstract, it is difficult to distinguish different gestures in the air simply by observing the waveform with the naked eye, and due to the differences in the behavior habits of different users, the signal waveform of the same gesture is also quite different, so the gesture recognition cannot only be based on the change of the signal value To make a judgment, it is necessary to mine the potential change law of the signal

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