## 5 Directions for Further Studies
The potential of computer vision in smart home applications is limited by the types of cameras. However, in order to collect an accurate 3D model of the environment considering the topographic situation of the ground, a combination of mobile, terrestrial, and airborne point cloud data sets seemsessential. A handheld scanner, such as a Zeb Revo tool generating point clouds, can be used for future studies, which can be processed further to find short paths for mobility-impaired people [86]. The collected data by a Zeb tool, demonstrated in Figure 14a,b, are used in fieldwork by two co-authors for indoor modeling of the buildings for construction [87; 88]. An example of terrestrial point clouds is also demonstrated in Figure 14c. Another important factor for developing such a model is to develop it within a GIS environment so that spatial analysis techniques can be applied to take advantage of georeferencing. Ground topographic data is required for the identification of the constraints in routes that decrease the level of mobility for the elderly. Lidar data as an accurate 3D data can be used for this purpose. To be able to use such data for determining the accessibility and connectivity of the network, the following proposed steps are suggested: (a) extract ground points from lidar data (such as the work done in [PERSON] et al. [89]). The extracted ground points then will be processed further to find slopes. (b) identify obstacles. After all the data comes from the accessibility, the connected routes achieved from texts and lidar data sets are collected and mapped, and an essential part is to compare these maps in terms of similarities. A union of the problematic areas from all maps could be a solution to overcome the possible observed dissimilarities among the maps. To analyze the collected text data spatially, there is a need to develop a GIS-based tool that can find and map words in categories of liked, disliked, and others mentioned by the elderly. In doing so, scholars can move further to identify hot spots of disliked areas, which are the locations that need considerations for improvement activities by city councils. Other technical developments for the GIS-based data analysis include the widget development for near real-time spatial data mining for the identification of hot spots of most used routes by the elderly and applying machine learning algorithms (such as the ones used by [PERSON] and [PERSON] [90; 91]) for finding coarse and fine obstacles. Considering all the information derived from the above-mentioned layers on the 3D models, appropriate paths for mobility-impaired people will be identified and will be ready to be imported to the navigation algorithm. The last GIS-based suggestion is to develop a flexible navigation algorithm. The identified 2 aCS routes will be used in our developed constrained-based navigation algorithm. Using such an algorithm for online navigation will enhance the capability of current navigations to find the most appropriate routes for mobility-impaired elderly. This algorithm has the flexibility to obtain updated data regularly. This article presented different typical plans using different technologies, but there are serious risks associated with these technologies. For example, the effect of living in highly dense wireless environments and many electronic devices working close to the body has not been fully investigated. Figure 14: Samples of Zeb point cloud data (**a,b**) and terrestrial point cloud data (**c**). Furthermore, smart devices could make things too perfect by taking off the element of surprise in every-day interactions, which could lead to psychological and emotional conflicts of smart home occupants [92]. Therefore, future studies should investigate the technical, physical, psychological, and emotional impacts of living in highly smart environments, as well as on aligning the design of smart homes, their devices, and AI algorithms to overcome any identified issues.