Abstract
In this paper, based on the wireless acceleration sensor, a wearable body data acquisition system is designed. The acceleration vector magnitude and the angular velocity vector amplitude signal are selected as the breakthrough of the body posture recognition. The focus is on the classification algorithms of the 10 body types commonly used by the soldiers, including Qi Bu walking, goose step, running, low posture, side posture, high posture, push-ups, sit-ups, upstairs and downstairs. The time domain features, frequency domain features and time-frequency characteristics of the signals are analysed respectively. The high-dimensional mixed feature vectors are extracted and reduced by LDA. A support vector machine algorithm based on hybrid features is proposed. The algorithm has been verified by experiments and achieved ideal results.
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