## 4 Input/Reactions from Participants
### Eye Gaze Tracking  
There are two major types of eye tracking technologies. The first one is head-mounted and the other is referred to as desktop based without requiring a participant to wear any device for eye tracking on his/her body. The core technology for eye tracking is identical to head-mounted and desktop eye trackers. An infrared camera is used to detect the rotations of the eyeballs (based on the pupil position and the corneal reflection). The reason why infrared light is used for eye movement tracking is that it renders a better demarcation of the pupil and the iris than visible light. Once the eye movements are determined, the gaze can be computed based on the context. In [37; 41; 121], an eye-tracker goggles from Arrington Research Inc. was used to track eye gaze in realtime. The goggles are used in conjunction with a custom designed eye tracking interface platform. The raw eye gaze data were sampled 30 times a second. Then, three features were extracted from the raw data, including the mean pupil diameter, the mean blink rate, and the average fixation duration for each region of interest. A head-mounted device called WearCam has been used to compute eye gaze offline. This device records the eyes of the participant and the image of the field of view in front of the participant simultaneously [122]. Based on the recorded data, the direction of gaze and focus of attention can be computed. A downside of WearCam is that it requires offline analysis of the recorded video to determine gaze. Hence, this device cannot be used if eye gaze needs to be determined in realtime. Eye gaze tracking can be made simpler if used in conjunction with head-mounted virtual reality devices, which produces a 360-degree immersive virtual 3D scene. In this case, it is sufficient to track the head movement, which is used in [28]. The head position monitor is integrated with the head-mounted display and it is used to measure the yaw (left/right), pitch (up/down), and tilt movements of the head. From these measurements, the eye gaze direction with respect to the virtual scene can be computed accurately. In [28], the social attention pattern of children with ASD is studied entirely using eye gaze as the sole input from the participants with a head-amounted eye tracker. Because desktop based eye-trackers are fixated to the monitor, eye tracking alone is not sufficient to accurately determine the gaze direction with respect to the computer monitor. Hence, desktop eye trackers typically integrate with head tracking based on face recognition to compensate for head movement, such as the Eye-Trac 6 desktop eye tracker from Applied Science Laboratories, which is used in [123] in their study. In [56; 60], a similar desktop eye tracking device, called Tobii X120, was used in their study. The device tracks at 120 Hz and allows free head movements within the range of 30 cm by 22 cm by 30 cm (for width, height and depth) and it requires the participant to sit at a distance of 70 cm. The technical specification for Tobii X120 does not state how the head orientation is determined. The authors of [60] designed and implemented an eye tracking application to be used in conjunction with the virtual reality application. The two applications are connected via a high-speed network such that they can determine which and if the participant fixated on one of the regions of interest of the avatar, including forehead, left/right eye, nose, mouth, other face areas, and non-face regions. The same eye tracking device was used in a study with social robots [85]. However, because of the different context where the study wanted to know whether or not the participant was looking at the robot instead of a virtual avatar displayed in a computer monitor, a technician was needed to manually observe the eye gaze and constantly calibrate the system. Automated eye gaze is possible even in this context.