A highprecision realtime human behavior recognition algorithm based on MEMS inertial sensor combination system is proposed in this paper. The time domain features of the inertial sensor combined system are extracted as feature parametersin this algorithm by selecting a sliding time window of 2 s as the feature extraction window, and the support vector machine based on a balanced decision tree is used to classify and recognize various human behavior patterns. The tests of the proposed algorithm are carried out on the wearable intelligent terminal platform independently developed by the laboratory. The resultsshow that the recognition rate of five walking behavior patterns and five nonwalking behavior patterns are up to 88% when the recognition time is reduced to 2 s/time. Compared with the existing algorithms, the realtime performance and accuracy of the proposed algorithm are significantly improved, and the types of pattern recognition are expanded.