This paper sets up a scenario where a monitor is operated through gestures and proposes a method that excludes false positives in gesture recognition using eye tracking data. A preliminary experiment confirmed that existing gesture recognition using acceleration data often misrecognizes relatively simple gestures like Circle and Check during activities involving significant movements, such as stretching and walking, compared with more complex gestures like Cross and Triangle. Additionally, eye tracking data were collected during daily activities. Analyzing these results, we hypothesized that a user’s gaze movements are small during intentional gestures aimed at operating a monitor. By calculating the standard deviation of various gaze features and excluding segments with abnormal values, we aimed to enhance the accuracy of gesture recognition. The results showed that features such as Fixation Movement Distance and Average Pupil Diameter Change are relatively effective in excluding false positives, particularly for simple gestures like Circle and Check.