The free-head 3D gaze tracking is of great significance. The traditional eye tracking methods have problems of low accuracy, complex equipment and limited wearing. To address these issues, a monocular head free 3D eye tracking technology based on machine learning is proposed. Two lightweight, high-precision and real-time eye tracking models are formulated, which can estimate the gaze point and gaze direction, respectively. For the gaze point estimation model, Dlib is used to locate the facial feature points to get the eye image. Then, PNP is used to get the head pose. Two kinds of information and part of the feature point coordinates are taken as the input into the multi-channel convolutional neural network. Finally, the gaze point is estimated. The gaze direction estimation network is a simplified version of gaze point estimation network. The proposed eye tracking technology is combined with the electric sickbed to establish a set of electric sickbed system based on eyeball drive, which allows patients to use their eyes to control the operation of the electric sickbed. Experimental results show that the error of the proposed gaze point estimate model on MPIIGaze dataset is 4. 1 cm. The error of gaze direction estimation network in ColumbiaGazedata set is 7. 2°, and the accuracy of the two models is 6. 8% and 2. 7% higher than those of iTracker and UlinFT, respectively. The system of eye driven electric sickbed can improve the living standard of patients and meet the needs of patients.