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With the development of head-mounted display devices, education and training based on virtual reality (VR) become popular. For education and training based on VR devices, there are interaction behaviors between users and devices. In these scenarios, it is particularly important for the VR devices to sensing the attitude of users. In order to ensure high-accuracy attitude resolution and reduce the systematic calculating cost, four attitude resolution methods based on the head-mounted inertial measurement unit (IMU) are designed in this paper. The comparison experiments of the four algorithms are given to evaluate their accuracy and efficiency. The experimental results demonstrate that the algorithm using third-order Taylor expansion recursive method to calculate the quaternion and the indirect extended Kalman filter to fuse the geomagnetic information outperforms the other three algorithms for the highest accuracy and the least operation time. The Euler angle calculated by the head attitude sensing algorithm based on the geomagnetic correction + third-order Taylor expansion method has an overall average deviation of 1.1 × 10-2 degrees from the attitude angle provided by the IG-500N IMU produced by SBG corporation, and the the time consumption of the algorithm is 5.1 seconds when 14000 sets of data are computed using MATLAB R2013a platform.