## 4 Results
### Modalities
#### 4.4.1 Input  
Table 6 lists the input modalities that were used in the reviewed studies, and each study is classified according to the input sources that are listed within each input modality. The \"Total\" column in Table 6 indicates the number of studies that used each modality among the 23 reviewed studies, while the rightmost column presents the percentage of studies listed in the \"Total\" column that used each of the input sources. \"Vision\" is a modality that every reviewed study utilized in their MI systems. There are many different input sources for using vision as an input modality. By analyzing each study, we identified four common input sources. First, 21 of 23 studies utilized vision as an input modality by using the movement of the camera view for AR. In order to visualize AR content, an agent moves the camera of an interaction device to locate an identifier within the camera view. The identifier can be either printed images used as AR markers [10; 34; 35; 36; 39; 40; 48], physical objects that can be recognized using object/image recognition [11; 12; 33; 34; 37; 38; 41; 42; 44; 46; 49; 50; 51; 52], or environments for using spatial data [39; 45]. The second identified input source is the camera view for capturing an agent's hands. We found that four studies used a camera feed to detect hand gestures [12; 41; 45; 49], while one study used a camera feed to track hand position [46]. The third input source is the agents' gaze movement as they move their heads. When an MI systems employs an HMD (e.g., HoloLens) as an interaction device, in some studies [41; 45; 49], the agents use the gaze position, which is the center point of their view moved along their heads, to interact with AR. The fourth input source is the position of real-world objects that is used to determine the position of AR content. For example, the MI system that was proposed by [PERSON] et al. [43] uses a smartboard that can detect smart objects using light density resistors installed under the smartboard. Based on the location that was detected by the light density resistors, the positions of the smart object on the smartboard are illustrated by AR on a TV screen that is connected to a stationary camera. This camera observes the smartboard, and the agent can watch the stream that contains AR content in real-time. The MI system that was proposed by [PERSON] et al. [44] also uses a camera feed to track the position of real-world objects when they are moved from their original positions. In addition, the MI system that was proposed by [PERSON] and [PERSON] [47] uses the camera view to identify the coordinates of real-world objects (i.e., crops) on a PC screen to position the AR interface near each identified real-world object. \"Touch\" is the second most used modality that we identified in 15 studies. In the taxonomy of modalities that was proposed by [PERSON] and [PERSON] [29], tactility refers to a device's ability to sense the physical contact of an agent. A touchscreen, mouse, and machine with buttons are examples of devices that can support interaction through tactility. We found that 13 of 15 studies used a touchscreen, whereas mouse clicks and buttons on machines were used in one study. The next modality category is \"kinesthetics\", which we found in eight studies. Kinesthetics can be divided into two different modalities--\"proprioception\" and \"kinematics\"--based on the taxonomy of modalities that was proposed by [PERSON] and [PERSON] [29]. Proprioception refers to a device's ability to detect the position and movement of a body part, while kinematics refers to a device's ability to sense the movement of a physical object based on acceleration. From an agent's perspective, any activity made with his or her hands or body that is not sensed by acceleration is related to proprioception as part of the interaction, whereas any movement sensed by acceleration sensors is related to kinematics as part of the interaction.