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Head magnetic resonance imaging (MRI) is susceptible to motion artifacts. Images with gross motion artifacts cannot be easily corrected, which makes the monitoring of patient movement is essential to improve the acquired image quality. Several methods have been proposed to detect patient movement during MRI scanning, such as navigator, optical tracking system and the image quality assessment. However, they have some disadvantages such as insufficient real-time performance and requiring additional MR data acquisition. Herein, we propose an MR-compatible millimeter wave (mmWave) radar system to monitor some typical types of movements during head MRI scanning. The radar sensor is installed in a 3T MR system. When MRI system scanning, the radar installed will vibrate. A novel algorithm is proposed to generate the spectrogram without vibration interference. Due to limited movement, we explore how the range information rather than micro-Doppler information can assist the activity classification. After data processing, convolutional neural network (CNN) is applied to extract the features in multiple range bins. The extracted features will be inputted to a shallow neural network for recognizing the moving parts. The result shows four different activities are classified with an overall accuracy of (96.81±0.69)%. Finally, experimental results with acquired head MR images prove our system's sufficient capability of recognizing nodding head yet inadequate capability of detecting head shaking movement.