## 4 Results
### Open Research Challenges
In their proposed MI system, crops on a farm were used as identifiers, and a number of stationary cameras were used to estimate the coordinates of the crops, which were then displayed on a PC monitor. The accuracy of coordinate estimation was high; however, [PERSON] and [PERSON] [47] noted a challenge involving solving the inaccuracy of coordinate estimation in a large-scale environment. In addition, [PERSON] et al. [40] noted out that adequate light conditions were necessary to place AR content in the correct position, while [PERSON] et al. [12] stated that the reflective surface of a target object caused a low success rate of object detection. Similar to [PERSON] et al. [12], [PERSON] et al. [48] mentioned the instability of vision-based tracking due to insufficient features of identifiers. Additionally, both [PERSON] et al. [40] and [PERSON] et al. [12] reported a short detection range for identifiers. The research challenges in the standardization category aim to improve MI systems while using a standardized data and system structure. For example, when each IoT device contains information about the target object for object detection, the MI system can easily register new IoT devices as long as all IoT devices use the same data structure, data exchange protocol, and UI structure [11; 50]. Furthermore, [PERSON] and [PERSON] [11] claimed that standardization is a way to achieve high scalability. In a similar vein, [PERSON] et al. [44] reported that reducing the computational costs using a distributed system is a way to enhance the scalability. [PERSON] et al. [44] also reported the necessity for an MI system to handle various interfaces with different IoT devices. Furthermore, [PERSON] et al. [48] claimed that MI systems require an automated IoT device registration process. The next keyword, multi-agent, refers to research challenges focused on a scenario in which multiple agents use the MI system. [PERSON] et al. [36] found that the preference for an interaction device differed, depending on an agent's physical state. For example, when an agent had a disorder of the lower body, he/she preferred a smartphone rather than a tablet PC due to the size of the device, which became burdensome holding it to observe identifiers for AR content. [PERSON] et al. [44] reported a data synchronization problem in the multi-agent scenario. In their MI system, the information of IoT devices could differ on each agent's interaction device due to a slow update rate. The update rate was designed for a single-agent scenario to reduce the computational costs. Therefore, [PERSON] et al. [44] planned to use a distributed system to reduce the computational costs while supporting a fast update rate. Furthermore, [PERSON] et al. [52] identified a need for personalized UI and feedback based on an agent's skill. In order to provide a personalized experience, [PERSON] et al. [52] noted that context-awareness was necessary in an MI system. The final keyword, multidisciplinary, refers to the necessity of collaboration with other academic disciplines. [PERSON] et al. [33] noted that knowledge of psychological aspects is necessary due to the different meaning of emojis, depending on the agent's culture. [PERSON] et al. [43] also emphasized the necessity of a multidisciplinary approach by highlighting the limitations of their study results. They developed an MI system to evaluate the upper limb function of an agent using an IoT object on a smartboard that can sense the location of an IoT object. They conducted a user evaluation; however, the agents did not have upper limb disorders; therefore, the efficiency of their MI system was not fully evaluated. Consequently, they mentioned the need for actual patients to verify the effectiveness of their MI system. We categorized this research challenge as multidisciplinary, because it is infeasible to find real patients for a user evaluation without the help of an individual from the medical field.