To solve the attitude drift, low real-time ability and high price problem in motion capture system based on inertial sensors, a kind of real-time motion capture system was designed to effectively overcome the attitude drift with low cost and power consumption. At first, a distributed joint motion capture node was built based on the human body kinematics principle, and every node worked in low-power mode, when the acquisition data from the node was lower than a predetermined threshold, the node would automatically enter into the sleep mode to reduce the power consumption of the system. In order to reduce the data drift in traditional algorithm, a kind of algorithm combined with inertial navigation and Kalman filter algorithm was designed to calculate the real-time motion data. Using the Wi-Fi module, the TCP-IP protocol was adopted to transmit the attitude data, which could drive the model in real time. At last, the accuracy of the algorithm was evaluated on the multi-axis motor test platform, and the effect of the system for tracking real human motion was compared. The experimental results show that the algorithm has higher accuracy by contrast with the traditional complementary filtering algorithm, which can control the angle drift in less than one degree; and the delay has no obvious lag by contrast with the complementary filter, which can realize the accurate tracking of human motion.