# Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance
## 6 Conclusions and Discussion  
This paper is dedicated to the R&D implications of the ETA wearable system (prototype) for navigation indoors for people who are BSVI. It emphasizes the novel outsourcing method of mapping and assistance for indoor routes. The logic and structure of the presented research follows certain steps, which can be narrated in the following way:After the overview of related research papers and patents, we found an evident lack of operationalization of the Web 2.0 social networking advantages for guided navigation indoors (see Introduction). That is, our investigation revealed that the indoor routing process could be crowdsourced to volunteers, avoiding costly infrastructural investment in RFID tags, Wi-Fi, beamers, etc. 2. To define the real social networking abilities and expectations concerning indoor navigation ETA assistance of people who are BSVI, we conducted a semi-structured survey of people who are BSVI and interviews of experts in the field (see Section 2). It clearly shaped and targeted our R&D efforts towards the development of a wearable ETA prototype with some key crowdsourced functions such as an indoor routing process and assistance from volunteers in complex situations. 3. Following the insights mentioned above and the needs of real BSVI users, we are in the process of constructing a unique computational, vision-based, wearable ETA prototype with crowdsourced functions. The presented wearable ETA prototype can be used in three consistent operational modalities: (i) sighted volunteers mark indoor routes using our wearable computational vision-based ETA prototype and help to maintain the web cloud DB of indoor routes; (ii) people who are BSVI can employ our wearable ETA prototype for guided navigation indoors using a chosen route from the route DB; and (iii) people who are BSVI can receive real-time web-crowd assistance (online volunteers' help via the mobile app) in complex situations (such as being lost and encountering unexpected obstacles and situations) using the wearable ETA prototype (see Sections 3-5). The applied research structure is user-centric and oriented toward actual BSVI needs. In this way, we avoided academic biases towards specific technology-centric approaches. Thus, the presented ETA indoor guided navigational system uses crowdsourcing when volunteers (sighted users) go through buildings and gather step-by-step visual and other sensory information on indoor routes that is processed using machine learning algorithms in the web cloud server and stored for BSVI usage in the web cloud DB. The proposed novel adaptation of crowdsourcing helps indoor routing services to be obtained from a large group of sighted participants (neighbors, friends, parents, etc. ), who voluntarily map indoor routes and place them in the online web server database. Here, indoor routes are processed using computational intelligence methods, enriched with semantic data, rated, and later exploited by people who are BSVI for indoor navigational guidance. We believe that this integration of crowdsourcing methods using social networking will drive a new R&D frontier that can make more efficient ETA indoor navigational applications for people who are BSVI. Social networking and outsourcing can facilitate the sharing and exchange of experiences with points of interest (POI) (such as stairs, doors, WC, entrances/exits), routes of interest (ROI), and areas of interest (AOI) indoors. This form of social networking could initiate the formation of a self-organized community of people who are BSVI and volunteers. In this way, participatory Web 2.0 social networking systems can integrate intelligent algorithms with the best experiences of people who are BSVI and sighted people while traveling, navigating, and orientating in indoor environments. This will help to build and continuously update real-time metrics of reachable POI, ROI, and AOI. This will allow routes to be averaged, erroneous routes eliminated, and optimal user-experience-based solutions found using various optimization approaches.