## 4 Results
### Open Research Challenges  
Through our analysis, we identified open research challenges that were mentioned in the reviewed studies. A total of 13 studies presented 19 research challenges, which we categorized based on the following keywords presented in Table 9: (1) technology, (2) standardization, (3) scalability, (4) multi-agent, and (5) multidisciplinary. Seven studies reported research challenges regarding hardware [37; 45; 47] and technical problems [12; 33; 40; 47; 48], which we summarized under Technology in Table 9. Research challenges regarding hardware problems concern either an interaction device [45], IoT device [37], or both [47]. For example, [PERSON] et al. [45] discovered that the narrow field of view of HoloLens makes it difficult for an agent to locate AR content \begin{table}
\begin{tabular}{l l c} \hline \hline
**Modality Combinations** & **Studies** & **\% of Total** \\ \hline Vision \& Touch: Tactility & [10; 11; 33; 34; 35; 36; 39; 40; 42; 47; 50; 51; 52] & 57 \\ Vision \& Audition & [12; 37; 38; 49] & 17 \\ Vision \& Kinesthetics: Kinematics & [43] & 4 \\ Vision \& Kinesthetics: Proprioception & [12; 41; 44] & 13 \\ Vision \& Touch: Tactility \& Kinesthetics: Proprioception & 9 \\ Vision \& Audition \& Kinesthetics: Proprioception & 49 \\ Vision \& Audition \& Kinesthetics: Kinematics, proprioception & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of modality combinations. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Keywords** & **Open Research Challenges** & **Studies** \\ \hline \multirow{8}{*}{Technology} & Narrow field of view in HoloLens & [45] \\  & Hardware performance problem regarding noise reduction & [37] \\  & High cost of technologies & [47] \\  & Obstacles for displaying non-visible identifiers & [33] \\  & Umereliable vision-based coordinate estimation in a large-scale environment & [47] \\  & Limited detection range for QR codes & [40] \\  & Requirement of specific light conditions for running AR properly & [40] \\  & Limited object detection range & [12] \\  & Low success rate of object detection on reflective surface & [12] \\  & Unstable vision-based feature tracking with insufficient features & [48] \\ \hline \multirow{2}{*}{Standardization} & Standardization of MI system components & [50] \\  & Standardization of AR feature data structure and data exchange protocol & [11] \\ \hline \multirow{3}{*}{Scalability} & Distributed system to reduce computational costs & [44] \\  & Management of diverse interfaces with different IoT devices & [44] \\  & Automation of IoT device registration process & [48] \\ \hline \multirow{3}{*}{Multi-agent} & Individual preference for interaction devices & [36] \\  & Data synchronization in multi-user scenario & [44] \\  & Personalized UI and feedback based on agent’s skill & [52] \\ \hline \multirow{2}{*}{Multidisciplinary} & Understanding of various cultures for different types of agents & [33] \\  & Conducting user evaluations in real-world scenario & [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of open research challenges. placed at the edge of the HoloLens screen. For IoT devices, [PERSON] et al. [37] reported a data quality problem for a heartbeat sensor due to ambient noise during data collection. [PERSON] and [PERSON] [47] noted the impracticality of installing the proposed MI system in an actual site due to the price of the devices and energy consumption. Other studies faced technical challenges, especially regarding the limitations of vision-based identifier detection [12; 33; 40; 47; 48]. For example, [PERSON] et al. [33] reported a challenge in displaying AR content when identifiers were blocked by environmental obstacles. Because the identifier detection system was vision-based, [PERSON] et al. [33] stated that identifiers that are not detected by the interaction device's camera must be handled in different ways. [PERSON] and [PERSON] [47] also noted a problem regarding a vision-based identifier detection system.