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Article

Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol

by
Emilio Alejandro Beltrán-Iza
,
Cristian Oswaldo Noroña-Meza
,
Alexander Alfredo Robayo-Nieto
,
Oswaldo Padilla
and
Theofilos Toulkeridis
*
Departamento de Ciencias de la Tierra y de la Construcción, Universidad de las Fuerzas Armadas ESPE, Sangolquí 171103, Ecuador
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(24), 17027; https://doi.org/10.3390/su142417027
Submission received: 10 November 2022 / Revised: 5 December 2022 / Accepted: 7 December 2022 / Published: 19 December 2022
(This article belongs to the Section Health, Well-Being and Sustainability)
Browse Figures
Review Reports Versions Notes

Abstract

:
The global navigation satellite systems (GNSS) have become important in conjunction with the advancement of technology, in order to improve the accuracy of positioning and navigation on mobile devices. In the current project, a mobile application for navigation using the network transport of restricted test case modeling (RTCM) via internet protocol (NTRIP) was developed, and it has been focused on the autonomous mobility of people with visual disabilities. This occurred through a web viewer that stores the base cartography in a genome database (GDB). Such information is integrated into the application interface with Java Script language within the Android Studio platform, with a personalized design. This incorporates a screen reader for selection, navigation and direction of destinations, in addition to an early warning system for obstacles. Additionally, a differential position correction was implemented using the BKG Ntrip Client (BNC) software, for the adjustment of coordinates with the precise point positioning (PPP) method through streams in the format of RTCM with casters EPEC3, IGS03 and BCEP00BKG0. The evaluation of the application was performed using the National Standard for Spatial Data Accuracy (NSSDA), establishing 30 control points. These were obtained through the fast static method, in order to compare the horizontal accuracy of the observations in static and navigation modes between high-end and mid-range mobile devices.

1. Introduction

The advancement of new technologies and the adaptability of users to intelligent tools is increasingly demanding, and thus, scientific development has contributed to academic and productive areas of the population [1,2,3,4,5]. The opposite occurs for people with visual disabilities, due to the dependence on third parties for different daily activities, which turn out to be more complex for this vulnerable group of society [6,7,8,9]. Worldwide, in 2020, there are some 43.3 million people among the population older than 50 years who are blind and some 295 million more who suffer from moderate to severe vision impairment, while many minorities are excluded from such databases [10,11]. Even more so, the incidence of high myopia and other eye-related diseases or weaknesses such as refractive errors and cataracts is increasing worldwide [12,13,14]. Hereby, vision impairment and blindness cause considerable global economic losses [15].
In Ecuador in 2021, with a total population of almost 18 million inhabitants, there are around 54,480 people with visual disabilities who are registered by the National Council for Equality of Disabilities (CONADIS) [16]. Of these, in the universities and polytechnic schools at the national level, there are 1188 vulnerable students incorporated within academic institutions [16].
Currently, mobile devices have positioned themselves globally, and while they are considered to be smart devices, they incorporate hardware and software that provide greater performance [17,18,19]. Cell phones have an accessibility service from software versions such as Android 6.0, so they serve as the main interface through which blind people may be able to interact, using a voice synthesizer which reads, explains, interprets and identifies what is displayed on the screen [20,21,22,23,24,25].
The integration of inclusive applications has been covered within the digital world, while local projects have focused on the autonomous and safe mobility of blind people, proposing the creation of a navigation assistant in public transport through Assistant GPS for Android. This allows the user to identify their location in real time, as well as the closest bus stops and main routes that are integrated [26,27,28,29]. The development of prototypes in apps such as TEUBICA provides the user with a better position and sense of orientation at the time of their journey, and apps can also include an integrated early warning system, as demonstrated in a project designed for outdoor areas in the cities of Panama [30].
Satellite navigation systems such as GNSS are responsible for providing geospatial positioning autonomously, so the accuracy of coordinates depends on characteristics of the tracking equipment and the type of information [31,32]. Through the GNSS network, position can be determined in four dimensions, which are longitude, latitude, height and time [33]. Currently, there are different constellations orbiting in outer space, such as GPS, GLONASS, BeiDou and Galileo [34,35,36,37]. The different methods for better precision, whether in postprocessing or real time, are static, fast static, RTK and NTRIP, among others [38,39,40].
The real-time positioning method network transport of restricted test case modeling (RTCM) via internet protocol (NTRIP) is based on the transmission of GNSS differential corrections, which are calculated from the reference or base stations [41,42,43,44]. The information is packaged in standard Radio Technical Commission for Marine Services (RTCM) format and is sent through the HTTP hypertext transfer protocol over the internet [45]. The NTRIP system is composed of up to four fundamental elements, being NTRIPSource, NTRIPServer, NTRIPCaster and NTRIPClient [46].
In previous studies, the usefulness of using corrections in real time through the NTRIP protocol was analyzed, comparing the quality of data with postprocessing methods [47,48]. The project was based on the implementation of a server with free software in charge of managing the messages sent from the reference stations, which authorize the reception of the format. Through scientific and experimental methods, satisfactory results were obtained, with submetric and centimetric precision [49,50].
In Ecuador, at the end of 2020, the Military Geographic Institute (IGM) allowed users access to the NTRIP protocol, where the GNSS Continuous Monitoring Network of Ecuador—REGME—is compatible with real-time positioning techniques, generating streams using the RTCM standard in versions 2.3 and 3.0 [50,51]. The positioning of base stations is linked to the official SIRGAS geocentric reference framework, ITRF 2008, with a reference period of 2016.14 [52]. The servers are located in the IGM in Quito and the Faculty of Computing and Electronics of the Escuela Politécnica de Chimborazo (ESPOCH) in Riobamba, central Ecuador, where they perform the function of caster principal and backup, respectively, with a shared port 2101. The release of the protocol allows reduction of operating costs for field activities, increased production, elimination of postprocessing in the cabinet and obtaining products such as reliable geoinformation services [53].
Based on the aforementioned, the predominant aim of the current study was to create a mobile application for navigation using the NTRIP protocol, focused on the autonomous mobility of people with visual disabilities, using pilot project established on the campus of an Ecuadorian university (Figure 1). In addition, we attempted to generate and classify detailed cartography based on obstacles, accessibility/mobility, infrastructure and green spaces, in order to create a GDB database through a GIS platform. Thus, the hardware and software conditions of the Android smartphone were also evaluated for the reception of GNSS systems in open spaces with NTRIP corrections, cell phone coverage and internet availability with the use of mobile data and Wi-Fi. Additionally, a web viewer was designed by identifying and assigning elements in the vector layers provided by the GDB database through the transfer to the web server of the GIS platform. Finally, the programming algorithms were generated to correct the position through the NTRIP protocol, allow early detection of obstacles and to identify optimal routes, with an approachable interface in the base structure of the Android mobile application. Everything listed was statistically evaluated for navigation accuracy in the application, using control points measured with GNSS receivers.
The contribution to development made by the current pilot project—an application focused on people with visual disabilities—is exploring new methodologies that allow greater precision in the recognition of destination points, that are capable of being employed in the physical environment. This shall guarantee the autonomy of blind or visually impaired people and, in turn, introduce a field in new inclusive or security applications. The project investigates the efficiency of determining location in real time through the NTRIP protocol, as a process of differential position correction within the navigation application. Further, this allows autonomy in users with visual disabilities, permitting them to recognize their environment through a database that classifies objects, obstacles, crossroads, stops and nearby areas on its route. Nonetheless, the main interest is improving the navigation services that cellular devices have, so the performance in positioning is still limited for people with visual disabilities, which is focused on applications that cover safe and efficient mobility.

2. Study Area

The integral design for the application for mobility of people with visual disabilities was conducted in the main campus of the University of the Armed Forces ESPE, located in the Sangolquí parish of the province of Pichincha, within the central area of the inter-Andean valley, in Ecuador (Figure 1). The institution has teaching staff, administrative staff and students totaling approximately 10,500 people who circulate during academic periods, and the campus has large spaces and permits easy mobility, under normal conditions. However, the process of inclusive spaces is still being implemented, and it does not cover all the spaces of interest for blind or visually impaired people. As part of a pilot project, we desired to demonstrate access to inclusive development spaces and the limitations that will be conditioned in the operation of the application for autonomous use in users who depend on assisted mobility, within open spaces, infrastructure, accesses and points of interest.

3. Methodology

The process for developing the pilot project for assisted mobilization in people with visual disabilities through an application consists of four axes as pillars for its operation, which work simultaneously to take advantage of the application. The structure of the geodatabase seeks detailed mapping of the intervention area to collect information that will be stored in a georeferenced database that allows the application to recognize its environment when the individual moves. The route design and display of the base map in a web viewer was implemented in order to consider the optimal displacements between a point of origin and destination. This occurred through a selection criterion based on time and distance parameters, classifying each segment for the recognition of the application. In turn, this information was stored in a web viewer that facilitates the supply of the application. Furthermore, we added the generation of GNSS corrections via NTRIP protocol and error vector simultaneously with the development of the application, of which the aim was to integrate the positioning adjustment of mobile devices. This allowed improvement of the accuracy of GPS chips, through corrections via the internet in real time. Additionally, the design of the application and registration of observations of mobile devices is based on evaluating the operating conditions of the application within Android devices, which were selected based on an approachable interface for users, allowing easy handling and easy orientation. Finally, for validation of the product, the control points were adjusted through fast static GNSS equipment positioning, which guarantees a positional accuracy of less than a decimeter, to compare the difference between the GNSS points and those obtained in the application.

3.1. Geodatabase Structure

The geodatabase was georeferenced in the WGS84 coordinate system with UTM 17S projection. The digitized information was obtained from an orthophoto of the year 2019 with a pixel size on the ground of 5 cm (GSD), at a frame scale of 1:5000, allowing cartographic products to be obtained at a scale of 1:1000. These facilitated the representation of great detail for each element, representing the basic geometric entities such as points, lines and polygons. The information was sorted by category, type of geometry, name, description of the field and domains of interest.

3.2. Design of Route Segments and Deploying the Base Map in a Web Viewer

The pedestrian mobility network was created for a network dataset by digitizing the main and alternate paths. The mobility structure describes the fields that condition and prioritize spaces of interest for people with visual disabilities (Figure 1). Costs by time and distance were considered. For the hierarchy of the network, it was classified into three categories, being primary, secondary and local for the highest, medium and lowest frequency accesses, respectively. Once the GDB was structured, the information was imported to the ESRI platform website at ArcGIS Online. The files are stored in compressed .zip format and with the original .gdb extension, representing a file collection structure in a folder that allows spatial and nonspatial data types to be stored, managed and consulted. The conceptualization of the base map provides information on the most representative entities of the campus (Figure 2).

3.3. GNSS Correction Generation via NTRIP Protocol (Receptor Base, Stream Connection, PPP)

The correction information was transmitted through the main caster server (receptor base) located in the Military Geographic Institute (IGM). The backup caster server was managed by the Faculty of Computer Science and Electronics of ESPOCH, facilitating the flow of RTCM streams in the continuous monitoring stations. The EPEC is a Trimble NEtR5 GNSS Continuous Monitoring Station (EMC–EPEC), which generates information in NMEA sentences and corrections through RTCM messages in versions 2.3 and 3.0 for the GPS and GLONAS constellations. The information was used for the BNC application that obtains the correction value of the three Cartesian components (∆X, ∆Y, ∆Z), necessary data to improve the precision in navigation of the mobile device. For the observation streams, the RTCM EPEC3 format version 3.0 was used, considering the area of influence of the station. Similarly, through the IGS International GNSS service, the positioning and correction of the GNSS constellations were obtained in real time from the products.igs-ip.net caster, with the BCEP00BKG0 and IGS03 streams.
The PPP fine point positioning adjustment method through BNC software integrates the streams of each caster for processing the corrected observations of the station. The process is performed in four phases (PPP 1 to PPP 4) that are integrated with each other, which are used to read, correct and store the EPEC information based on the IGS caster. PPP 1 corresponds to the input and output of information, where the flows and files required by BNC for real-time or PPP postprocessing are specified. In PPP 2, the specific parameters for each process are entered. Individual sigmas can be entered for prior coordinates, estimates from the troposphere and a noise for coordinate variations over time. This study used the default values of the application for the PPP process, so there is no local tropospheric and noise model. PPP 3 allows the recording of specific sigmas for code and phase observations. There, the general processing options are specified linear combinations (GNSS–LCs), and ionosphere-free observations in which ambiguity resolutions are to be performed are detailed. The specification must be made according to the GNSS system (“GPS LC”, “GLONASS LC”, “Galileo LC”, “BDS LC”). The EPEC station tracks the GPS and GLONASS constellation signals, so the Galileo and BeiDou constellations were disabled in the analysis. Finally, the PPP 4 displays the position of the base station defined by means of its ID for analysis through a web viewer connected to OpenStreetMap (OMS). The processing for corrections in PPP indicates the tracking of observations, indicating the variation of coordinates, in a topocentric system (enu), for a period of time. The postprocessing speed specifies the rendering ability of the station’s observations, as appropriate to the DPI adjustment calculations (Figure 3).

3.4. Error Vector

The difference was established between the EPEC continuous monitoring station with known coordinates ( X B a s e ) and the observed coordinates adjusted through the NTRIP correction protocol ( X C / I n s t ) for an instant of time t, which allows generation of the components ∆X, ∆Y and ∆Z (Equation (1)). The deltas of each component are known as the error vector (Figure 4) [54].
∆ = X C / I n s t − X B a s e
The parameters that are used for differential position correction in mobile devices are the tolerance range less than 20 km, equal or similar number of tracked satellites and establishment of the same UTC time of the records for the station and the cell phone. If the parameters are met, the corrected coordinate is calculated by means of the difference between the observed coordinate and the variation deltas ∆X, ∆Y and ∆Z (Figure 5).

3.5. Design of the Mobile Application and Record of Observations of Mobile Devices

The source code of the mobile application was developed on the Android Studio platform. The design of the programming language for the application was executed in Java format, with the ability to work with application programming interfaces (APIs), XML/HTML and database connections. However, based on the 30 control points (GCP), the statistics of the measures of central tendency and dispersion were analyzed. It was compared between two cell phones with Android operating systems, being a Xiaomi Poco F3 (high-end) and a Huawei Mate 20 Lite (mid-range), in order to yield the precision error of each device, while the quality of data for the positioning methods was evaluated in static and navigation modes. Table 1 describes the main characteristics of the devices related to the benefits of positioning according to the conditions of the GPS chip.
For data recording, the mobile devices were compared at the same moment of time through the GPS TEST application. The same external conditions and satellite tracking were established. The positioning information was stored in NMEA format, with an update interval every second. It also displayed information on the satellites in use for each constellation, as well as frequency and signals that are used to determine location (Figure 6). The data recording was performed for a single instant in time, storing the information for that point. Once the route was finished, while the devices were previously conditioned for continuous tracking, the file was stored in NMEA format with GGA sentences.

3.6. Adjusting GCP Control Points

Thirty control points were taken as a positioning sample. The number of points was considered based on the NSSDA standard. They were densified inside the campus, with a greater concentration in built-up areas, for the purpose of validating the digitization of base cartography and determining the accuracy of navigation on mobile devices through the proposed application (Figure 7). The ranges of precision on mobile devices and navigation are based on the design application as illustrated in Figure 7.
The coordinate adjustment in postprocessing used the Trimble base antenna observation files, those of the EMC EPEC and ephemeris. The observation format is found with the .T02 extension, and the fast igr ephemeris with the .sp3 extension, obtained through the Crustal Dynamics Data Information System CDDIS portal, in the GPS week and days igr21875 and igr21876. In the same way, the observation files of the station are available in the IGM Geoportal with names EPEC344 and EPEC345, for the GPS week 2183 with reference frame IGS14 (Table 2). The adjustment of the observed points was realized through the commercial software Trimble Business Center (TBC) version 2.5.

3.7. Horizontal Accuracy

For calculation of horizontal positional accuracy, the National Standard for Spatial Data Accuracy, NSSDA, methodology compares the observed and corrected values of the mobile device, with respect to the thirty control points proposed in the project. The Cartesian coordinates are transformed to a local topocentric coordinate system with east, north and height (enu) components, which allows a more realistic representation on the ground and represents the variation of the components graphically in terms of horizontal distance for corrected navigation data. This is in contrast to the static method, where only the raw observations were considered.

4. Results

4.1. Static Positioning

The results obtained from the coordinates tracked with the Xiaomi Poco F3 (Figure 8 and Figure 9) and Huawei Mate 20 Lite (Figure 10 and Figure 11) cell phones, over a period of five minutes, were compared using the control points established in the spaces of interest within the parent campus. The behavior of the data is described in topocentric coordinates referred to the EPEC continuous monitoring station, for the control points and the tracking observations, comparing the dispersion in each of its east, north and height components.

4.1.1. The Case of Xiaomi Poco F3

For each delta of the topocentric components (∆e, ∆n, ∆u) there is a mean of 2.71, −1.43 and 4.14, respectively, with a standard deviation around ±1.7 m for east and north, unlike the height that has a standard deviation close to 5 m. The maximum and minimum values of each component indicate the degree of displacement with respect to the known point (Table 3).
However, the horizontal distance (HD) more representatively describes the observed coordinates, with a mean of 3.60 m, a standard deviation ±1.39 m and a range of 5.54 m. The probability distribution of data with respect to HD indicates a symmetric distribution with a value of 0.008, together with the kurtosis coefficient of −0.778, where there is a lower concentration of data around the mean (Figure 10).

4.1.2. The Case of Huawei Mate 20 Lite

For each delta of the topocentric components ∆e, ∆n and ∆u, a mean of 0.86, 0.00 and 0.78 m is indicated, respectively, with a standard deviation greater than 5 m for east and north, unlike the height represented by a standard deviation close to 12 m. The maximum and minimum values of each component indicate the degree of displacement with respect to the known point (Table 4).
However, the horizontal distance (HD) more representatively describes the observed coordinates, with a mean of 6.26 m, a standard deviation ±4.02 m and a range of 18.48 m. The distribution of probability data with respect to the HD indicates a moderate asymmetry with a value of 0.66 with a positive bias to the left, together with the kurtosis coefficient of −0.5, where the data are grouped around the mean (Figure 11).

4.2. Navigation

The adjustment of observations was made for the navigation data, which were recorded at the end of the route to the control point. The recording time of the data referring to the EPEC station was carried out on 23 January 2022 with a tracking time of three hours, which allows comparison between the observed points and those adjusted by applying the correction vector for the positioning of the points from mobile devices (Table 5 and Table 6).

4.2.1. The Case of Xiaomi Poco F3

The variation between the observed and corrected data presents similarities in the descriptive analysis—the adjusted means are displaced −0.107 and 0.115 m for east and north, respectively. The standard deviation for each component does not display a significant change between both conditions. The maximum and minimum values were considered as indicators of the displacement that each component suffered independently for the representation of horizontal dispersion (Table 6). The descriptive analysis for the horizontal dispersion between observed and corrected data does not present a relevant change in the behavior of results. The means have a displacement difference of 0.09 m, and the standard deviation presents an increase of 0.03 m, relating to the range decrease of 0.12 m. The dispersion of the corrected data is affected, with a variation of 40.1% compared to the observations with 38.8%. Figure 12 indicates the difference between the observations and their corrections.

4.2.2. The Case of Huawei Mate 20 Lite

The variation between the observed and corrected data presents similarities in the descriptive analysis, as the adjusted means are displaced 0.107 and −0.112 m for east and north, respectively. The standard deviation for each component does not exhibit significant change between both conditions, while the maximum and minimum values were considered as indicators of the displacement suffered by each component independently. The data of the representation of horizontal dispersion are listed in Table 7 and Table 8. Figure 13 indicates the difference between the observations and their corrections.
The descriptive analysis for horizontal dispersion between the observed and corrected data does not present a relevant change in the behavior of results. The means have a displacement difference of 0.068 m, while the standard deviation presents a decrease of 0.045 m, relating to the range reduction of 0.348 m. The dispersion of the corrected data is affected, with a variation of 40.9% compared to the observations with 41.2%.

4.3. Horizontal Positional Accuracy

The horizontal error of each point is described to determine the horizontal positional accuracy. Table 9 indicates the difference of the mean square error (RMSE) for the east and north components of the observations recorded by the Xiaomi and Huawei devices, as well as their total error (RMSE horizontal) and their respective precisions. The observations of the static positioning compared for both cell phones present a better precision for the Xiaomi device, reaching values of ±6.59 m, unlike the mid-range cell phone, Huawei, which presented a lower-quality precision of ±12.88 m. The horizontal mean square error indicates the comparison between the known data and its observations, being the RMSE H of 3.86 m and 7.44 m between Xiaomi and Huawei mobile devices, respectively. Nonetheless, it is true that a distance error of 3.86 m still could mean life and death for a visually impaired person. Although our approach is interesting and novel, there is more work required before it could be implemented in a real-life case for people with visual impairment.
The navigation data compared to the static positioning data present a representative change in their accuracies. The Xiaomi device increases its error by approximately 3 m, affecting the quality of the observations, unlike the Huawei device, which improves precision, reducing its initial value by approximately 2.5 m. However, the navigation quality prevails for the Xiaomi Poco F3 device with an accuracy of 9.62 m compared to 10.35 m for the Huawei Mate 20 Lite cell phone (Figure 14).
The corrections for navigation observations improve the precision, incorporating correction vectors in the mobile devices, but they do not indicate a significant change when compared to their initial values, presenting a reduction of 0.12 and 0.14 m for the Xiaomi and Huawei cell phones, respectively. An error magnitude was obtained for Xiaomi and Huawei devices, with lower precision in built-up areas, directly affecting the positioning capacity of the equipment, unlike open spaces, which guarantee a more precise positioning coverage (Figure 14).

5. Discussion

Unlike other applications present in virtual markets, in applications focused on autonomy for visually impaired people, it is evident that their interface, despite being more dynamic, does not provide a complete service for its users. Among its main problems is the architecture of the base information for deployment of geographic information, which is due to the fact that its programming uses APIs of the Google service [55,56,57,58]. Even more so, the base cartography does not constitute an exact representation of pedestrian viability—because it is a global service, its information represents the demand of the majority of users [59,60,61]. Therefore, it does not present greater definition when addressing pedestrian paths because of the complexity that this entails during mapping. In the design of the pilot app, the development of a road network that guarantees positioning within established spaces for pedestrian mobility is presented as a contribution, considering that the user does not possess a sense of orientation within their environment for their displacement.
Precision is fundamental to guarantee autonomy and safety in its use. For this, we opted for implementation with the data transport network in RTCM format through internet protocol NTRIP for the real-time positioning of the user, which, unlike other applications, uses APIs to correct the raw observations through established codes that facilitate the adjustment of observations. However, the precision achieved is not sufficient to satisfy the demand of a visually impaired person. Such limitations could be overcome by studies that involve augmenting GPS with geolocated fiducials to improve accuracy of mobile robot applications, and mobile robot localization may be better reached and realized by using GPS, inertial measurement unit (IMU) and visual odometry or using apps with such services [62,63,64,65].

6. Conclusions

In the current study, we were able to create an application where we implemented real-time position correction using the NTRIP protocol with a higher accuracy than before, as vision-impaired people need such accuracy in order to avoid potential obstacles and stay on secured paths. Additionally, we were able to update and improve the pedestrian road design via GIS Pro, which is an additional service for the people who will use our app.
Nonetheless, simultaneously, we encountered some limitations, such as the fact that location and navigation information permissions (GPS-GNSS access) in mobile device operating systems are limited for implementation in differential corrections. Furthermore, detailed cartographic information for route design and obstacle detection was often not digitized or updated for the areas of interest. An additional limitation is the availability of devices with GNSS tracking characteristics and time for collection and interpretation of the data recorded every second at different control points.
In the future, we intend to develop the application on a larger scale and with free access for different operating systems. In addition, the corrections present an acceptable improvement according to statistical analysis, allowing the methodology to be implemented in new technologies and applications that use GPS and to provide beneficial navigation for all users in general. Such challenges will be addressed by augmenting differential GPS, IMU and/or visual odometry with geolocated fiducials, which will allow improved accuracy of mobile robot applications, which is especially important for people with limited or impaired views and other persons of such needs.

Author Contributions

Conceptualization, E.A.B.-I., C.O.N.-M. and A.A.R.-N.; methodology, A.A.R.-N. and O.P.; validation, A.A.R.-N. and O.P.; formal analysis, E.A.B.-I. and C.O.N.-M.; investigation, E.A.B.-I. and C.O.N.-M.; data curation, A.A.R.-N. and O.P.; writing—original draft preparation, E.A.B.-I., C.O.N.-M. and T.T.; writing—review and editing, T.T.; visualization, E.A.B.-I. and C.O.N.-M.; supervision, A.A.R.-N. and O.P.; project administration, A.A.R.-N.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Garetti, M.; Taisch, M. Sustainable manufacturing: Trends and research challenges. Prod. Plan. Control 2012, 23, 83–104. [Google Scholar] [CrossRef]
  2. Chatti, M.A.; Dyckhoff, A.L.; Schroeder, U.; Thüs, H. A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 2012, 4, 318–331. [Google Scholar] [CrossRef]
  3. Yang, G.Z.; Bellingham, J.; Dupont, P.E.; Fischer, P.; Floridi, L.; Full, R.; Jacobstein, N.; Kumar, V.; McNutt, M.; Merrifield, R.; et al. The grand challenges of Science Robotics. Sci. Robot. 2018, 3, eaar7650. [Google Scholar] [CrossRef] [PubMed]
  4. Siemens, G. Learning analytics: The emergence of a discipline. Am. Behav. Sci. 2013, 57, 1380–1400. [Google Scholar] [CrossRef] [Green Version]
  5. Toulkeridis, T.; Porras, L.; Tierra, A.; Toulkeridis-Estrella, K.; Cisneros, D.; Luna, M.; Carrión, J.L.; Herrera, M.; Murillo, A.; Salinas, J.C.P.; et al. Two independent real-time precursors of the 7.8 Mw earthquake in Ecuador based on radioactive and geodetic processes—Powerful tools for an early warning system. J. Geodyn. 2019, 126, 12–22. [Google Scholar] [CrossRef]
  6. Baker, S.M.; Stephens, D.L.; Hill, R.P. Marketplace experiences of consumers with visual impairments: Beyond the Americans with Disabilities Act. J. Public Policy Mark. 2001, 20, 215–224. [Google Scholar] [CrossRef] [Green Version]
  7. Laforge, R.G.; Spector, W.D.; Sternberg, J. The relationship of vision and hearing impairment to one-year mortality and functional decline. J. Aging Health 1992, 4, 126–148. [Google Scholar] [CrossRef]
  8. Jessup, G.M.; Bundy, A.C.; Cornell, E. To be or to refuse to be? Exploring the concept of leisure as resistance for young people who are visually impaired. Leis. Stud. 2013, 32, 191–205. [Google Scholar] [CrossRef]
  9. Gelberg, L.; Andersen, R.M.; Leake, B.D. The Behavioral Model for Vulnerable Populations: Application to medical care use and outcomes for homeless people. Health Serv. Res. 2000, 34, 1273. [Google Scholar]
  10. Bourne, R.; Steinmetz, J.D.; Flaxman, S.; Briant, P.S.; Taylor, H.R.; Resnikoff, S.; Casson, R.J.; Abdoli, A.; Abu-Gharbieh, E.; Afshin, A.; et al. Trends in prevalence of blindness and distance and near vision impairment over 30 years: An analysis for the Global Burden of Disease Study. Lancet Glob. Health 2021, 9, e130–e143. [Google Scholar] [CrossRef]
  11. Fernandes, A.G.; Alves, M.; Valdrighi, N.Y.; de Almeida, R.C.; Nakano, C.T. Visual impairment and blindness in the Xingu Indigenous Park–Brazil. Int. J. Equity Health 2021, 20, 197. [Google Scholar] [CrossRef] [PubMed]
  12. Ackland, P.; Resnikoff, S.; Bourne, R. World blindness and visual impairment: Despite many successes, the problem is growing. Community Eye Health 2017, 30, 71. [Google Scholar] [PubMed]
  13. Hosoda, Y.; Yoshikawa, M.; Miyake, M.; Tabara, Y.; Shimada, N.; Zhao, W.; Oishi, A.; Nakanishi, H.; Hata, M.; Akagi, T.; et al. CCDC102B confers risk of low vision and blindness in high myopia. Nat. Commun. 2018, 9, 1782. [Google Scholar] [CrossRef] [Green Version]
  14. Reis, T.; Lansingh, V.; Ramke, J.; Silva, J.C.; Resnikoff, S.; Furtado, J.M. Cataract as a cause of blindness and vision impairment in Latin America: Progress made and challenges beyond 2020. Am. J. Ophthalmol. 2021, 225, 1–10. [Google Scholar] [CrossRef]
  15. Marques, A.P.; Ramke, J.; Cairns, J.; Butt, T.; Zhang, J.H.; Muirhead, D.; Jones, I.; Tong, B.A.A.; Swenor, B.K.; Faal, H.; et al. Global economic productivity losses from vision impairment and blindness. eClinicalMedicine 2021, 35, 100852. [Google Scholar] [CrossRef] [PubMed]
  16. CONADIS. Estadísticas de Discapacidad—Consejo Nacional para la Igualdad de Discapacidades. Ministerio de Salud Publica. 2021. Available online: https://www.consejodiscapacidades.gob.ec/estadisticas-de-discapacidad/ (accessed on 14 October 2021).
  17. Porter, M.E.; Heppelmann, J.E. How smart, connected products are transforming companies. Harv. Bus. Rev. 2015, 93, 96–114. [Google Scholar]
  18. Abdullah, A.; Ismael, A.; Rashid, A.; Abou-ElNour, A.; Tarique, M. Real time wireless health monitoring application using mobile devices. Int. J. Comput. Netw. Commun. 2015, 7, 13–30. [Google Scholar] [CrossRef]
  19. Sung, Y.T.; Chang, K.E.; Liu, T.C. The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Comput. Educ. 2016, 94, 252–275. [Google Scholar] [CrossRef] [Green Version]
  20. Zhang, H.; Yuan, Y.; Li, W.; Zhang, B.; Ou, J. A grid-based tropospheric product for China using a GNSS network. J. Geod. 2018, 92, 765–777. [Google Scholar] [CrossRef]
  21. Vendome, C.; Solano, D.; Liñán, S.; Linares-Vásquez, M. Can everyone use my app? An empirical study on accessibility in android apps. In Proceedings of the 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), Cleveland, OH, USA, 29 September–4 October 2019; IEEE: Piscataway, NJ, USA; 2019; pp. 41–52. [Google Scholar]
  22. Alshayban, A.; Ahmed, I.; Malek, S. Accessibility issues in android apps: State of affairs, sentiments, and ways forward. In Proceedings of the 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), Seoul, Republic of Korea, 16–24 July 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1323–1334. [Google Scholar]
  23. Damaceno, R.J.P.; Braga, J.C.; Mena-Chalco, J.P. Mobile device accessibility for the visually impaired: Problems mapping and recommendations. Univers. Access Inf. Soc. 2018, 17, 421–435. [Google Scholar] [CrossRef]
  24. Branham, S.M.; Mukkath Roy, A.R. Reading between the guidelines: How commercial voice assistant guidelines hinder accessibility for blind users. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility; Association for Computing Machinery: New York, NY, USA, 2019; pp. 446–458. [Google Scholar]
  25. Costa, L.C.; Correa, A.G.; Dalmon, D.L.; Zuffo, M.K.; Lopes, R.D. Accessible educational digital book on tablets for people with visual impairment. IEEE Trans. Consum. Electron. 2015, 61, 271–278. [Google Scholar] [CrossRef]
  26. Bıyık, C.; Abareshi, A.; Paz, A.; Ruiz, R.A.; Battarra, R.; Rogers, C.D.; Lizarraga, C. Smart mobility adoption: A review of the literature. J. Open Innov. Technol. Mark. Complex. 2021, 7, 146. [Google Scholar] [CrossRef]
  27. Martínez-Cruz, S.; Morales-Hernández, L.A.; Pérez-Soto, G.I.; Benitez-Rangel, J.P.; Camarillo-Gómez, K.A. An Outdoor Navigation Assistance System for Visually Impaired People in Public Transportation. IEEE Access 2021, 9, 130767–130777. [Google Scholar] [CrossRef]
  28. Kuriakose, B.; Shrestha, R.; Sandnes, F.E. Smartphone navigation support for blind and visually impaired people-a comprehensive analysis of potentials and opportunities. In International Conference on Human-Computer Interaction; Springer: Cham, Switzerland, 2020; pp. 568–583. [Google Scholar]
  29. Khenkar, S.; Alsulaiman, H.; Ismail, S.; Fairaq, A.; Jarraya, S.K.; Ben-Abdallah, H. ENVISION: Assisted navigation of visually impaired smartphone users. Procedia Comput. Sci. 2016, 100, 128–135. [Google Scholar] [CrossRef] [Green Version]
  30. Tristán, G.D.; Arcia, A.; Montes Franceschi, H.; Pérez, R. Aplicación móvil para el monitoreo de personas con discapacidad visual. Tecnol. Accesibilidad 2016, 1, 93–100. [Google Scholar]
  31. Kuutti, S.; Fallah, S.; Katsaros, K.; Dianati, M.; Mccullough, F.; Mouzakitis, A. A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet Things J. 2018, 5, 829–846. [Google Scholar] [CrossRef]
  32. Kealy, A.; Retscher, G.; Toth, C.; Hasnur-Rabiain, A.; Gikas, V.; Grejner-Brzezinska, D.; Danezis, C.; Moore, T. Collaborative navigation as a solution for PNT applications in GNSS challenged environments–report on field trials of a joint FIG/IAG working group. J. Appl. Geod. 2015, 9, 244–263. [Google Scholar] [CrossRef] [Green Version]
  33. Forootan, E.; Dehvari, M.; Farzaneh, S.; Khaniani, A.S. A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity fields in the lower atmosphere using GNSS measurements. Adv. Space Res. 2021, 68, 4024–4038. [Google Scholar] [CrossRef]
  34. Li, X.; Ge, M.; Dai, X.; Ren, X.; Fritsche, M.; Wickert, J.; Schuh, H. Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. J. Geod. 2015, 89, 607–635. [Google Scholar] [CrossRef] [Green Version]
  35. Li, X.; Zhang, X.; Ren, X.; Fritsche, M.; Wickert, J.; Schuh, H. Precise positioning with current multi-constellation global navigation satellite systems: GPS, GLONASS, Galileo and BeiDou. Sci. Rep. 2015, 5, 8328. [Google Scholar] [CrossRef] [Green Version]
  36. Pan, L.; Zhang, X.; Li, X.; Li, X.; Lu, C.; Liu, J.; Wang, Q. Satellite availability and point positioning accuracy evaluation on a global scale for integration of GPS, GLONASS, BeiDou and Galileo. Adv. Space Res. 2019, 63, 2696–2710. [Google Scholar] [CrossRef]
  37. Pan, L.; Zhang, X.; Liu, J.; Li, X.; Li, X. Performance evaluation of single-frequency precise point positioning with GPS, GLONASS, BeiDou and Galileo. J. Navig. 2017, 70, 465–482. [Google Scholar] [CrossRef]
  38. Bellone, T.; Dabove, P.; Manzino, A.M.; Taglioretti, C. Real-time monitoring for fast deformations using GNSS low-cost receivers. Geomat. Nat. Hazards Risk 2016, 7, 458–470. [Google Scholar] [CrossRef]
  39. Kasat, N.; Dharmappa, D.; Singh, F.B.; Ramakrishna, B.N. Static Coordinate Estimation Techniques Based on Satellite Navigation. In Proceedings of the 2019 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), Mysuru, India, 13–14 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 64–69. [Google Scholar]
  40. Li, Z.; Zhang, J.; Li, T.; He, X.; Wu, M. Analysis of static and dynamic real-time precise point positioning and precision based on SSR correction. In Proceedings of the 2016 IEEE International Conference on Information and Automation (ICIA), Ningbo, China, 1–3 August 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 2022–2027. [Google Scholar]
  41. Luna, M.P.; Staller, A.; Tierra, A.; Molina, X.; Toulkeridis, T. Analysis of statistical interpolation methods to generate the velocities model for continental Ecuador from GNSS data. Rev. Geogr. Venez. 2022; in press. [Google Scholar]
  42. Zhang, X.; Ross, A.S.; Fogarty, J. Robust annotation of mobile application interfaces in methods for accessibility repair and enhancement. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, Berlin, Germany, 14–18 October 2018; Association for Computing Machinery: New York, NY, USA, 2018; pp. 609–621. [Google Scholar]
  43. Um, I.; Park, S.; Oh, S.; Kim, H. Analyzing Location Accuracy of Unmanned Vehicle According to RTCM Message Frequency of RTK-GPS. In Proceedings of the 2019 25th Asia-Pacific Conference on Communications (APCC), Ho Chi Minh City, Vietnam, 6–8 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 326–330. [Google Scholar]
  44. Lee, Y.C. Assessing the Real-time Positioning Accuracy of Low-cost GPS Receiver using NTRIP-based Augmentation Service. J. Korean Soc. Geospat. Inf. Sci. 2015, 23, 31–39. [Google Scholar]
  45. Vana, S.; Aggrey, J.; Bisnath, S.; Leandro, R.; Urquhart, L.; Gonzalez, P. Analysis of GNSS correction data standards for the automotive market. Navigation 2019, 66, 577–592. [Google Scholar] [CrossRef]
  46. Stürze, A.; Mervart, L.; Weber, G.; Rülke, A.; Wiesensarter, E.; Neumaier, P. The new version 2.12 of BKG Ntrip Client (BNC). Geophys. Res. Abstr. 2016, 18, 12012. [Google Scholar]
  47. Erol, S.; Alkan, R.M.; Ozulu, İ.M.; İlçi, V. Performance analysis of real-time and post-mission kinematic precise point positioning in marine environments. Geod. Geodyn. 2020, 11, 401–410. [Google Scholar] [CrossRef]
  48. Wang, Z.; Li, Z.; Wang, L.; Wang, X.; Yuan, H. Assessment of multiple GNSS real-time SSR products from different analysis centers. ISPRS Int. J. Geo-Inf. 2018, 7, 85. [Google Scholar] [CrossRef] [Green Version]
  49. Carranza Carranza, A.A.; Reyes Orozco, J.A. Análisis en Implementación de Diferencial de GPS en Tiempo Real a Través de a Tecnología NTRIP para la EERSA [Escuela Superior Politécnica de Chimborazo]. 2017. Available online: http://dspace.espoch.edu.ec/handle/123456789/8434 (accessed on 6 February 2022).
  50. Cisneros, D.; Zabala, M.; Oñate, A. REGME-IP Real Time Project. In Proceedings of the The International Conference on Advances in Emerging Trends and Technologies, Riobamba, Ecuador, 26–31 October 2020; Springer: Cham, Switzerland, 2020; pp. 42–57. [Google Scholar]
  51. Liu, Q.; Hernández-Pajares, M.; Yang, H.; Monte-Moreno, E.; Roma-Dollase, D.; García-Rigo, A.; Li, Z.; Wang, N.; Laurichesse, D.; Blot, A.; et al. The cooperative IGS RT-GIMs: A reliable estimation of the global ionospheric electron content distribution in real time. Earth Syst. Sci. Data 2021, 13, 4567–4582. [Google Scholar] [CrossRef]
  52. Bornes, S.R.; Báez, J.C.; Castro, H.M.; Pichuante, I.P.; Norambuena, C.R. Red Geodésica Nacional y la transformación a SIRGAS–Chile, realización de 2013.0 a 2016.0 para cartografía y Sistemas de Información Geográfica. Rev. Geogr. Chile Terra Aust. 2021, 57, 88–94. [Google Scholar]
  53. IGM. A Finales 2020, Servicio de Correcciones Diferenciales en Tiempo Real, Protocolo NTRIP—Inicio. Instituto Geográfico Militar, 1. Available online: http://www.geograficomilitar.gob.ec/a-finales-2020-servicio-de-correcciones-diferenciales-en-tiempo-real-protocolo-ntrip/ (accessed on 21 November 2021).
  54. Silva Villacrés, O.F. Implementación de la Tecnología NTRIP en Dispositivos Móviles Navegadores, Mediante una Aplicación, para Obtener Coordenadas GPS con Mejor Presición en Tiempo Real [Universidad de las Fuerzas Armadas ESPE]. 2014. Available online: http://repositorio.espe.edu.ec/handle/21000/8479 (accessed on 21 November 2021).
  55. Kammoun, S.; Parseihian, G.; Gutierrez, O.; Brilhault, A.; Serpa, A.; Raynal, M.; Oriola, B.; Macé, M.M.; Auvray, M.; Denis, M.; et al. Navigation and space perception assistance for the visually impaired: The NAVIG project. IRBM 2012, 33, 182–189. [Google Scholar] [CrossRef]
  56. Real, S.; Araujo, A. Navigation systems for the blind and visually impaired: Past work, challenges, and open problems. Sensors 2019, 19, 3404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Zimmermann-Janschitz, S. The application of geographic information systems to support wayfinding for people with visual impairments or blindness. In Visual Impairment and Blindness-What We Know and What We Have to Know; IntechOpen: London, UK, 2019. [Google Scholar]
  58. Ghali, N.I.; Soluiman, O.; El-Bendary, N.; Nassef, T.M.; Ahmed, S.A.; Elbarawy, Y.M.; Hassanien, A.E. Virtual reality technology for blind and visual impaired people: Reviews and recent advances. Adv. Robot. Virtual Real. 2012, 26, 363–385. [Google Scholar]
  59. Rizzo, J.R.; Beheshti, M.; Fang, Y.; Flanagan, S.; Giudice, N.A. COVID-19 and visual disability: Can’t look and now don’t touch. PM R 2021, 13, 415–421. [Google Scholar] [CrossRef]
  60. Legge, G.E.; Beckmann, P.J.; Tjan, B.S.; Havey, G.; Kramer, K.; Rolkosky, D.; Gage, R.; Chen, M.; Puchakayala, S.; Rangarajan, A. Indoor navigation by people with visual impairment using a digital sign system. PLoS ONE 2013, 8, e76783. [Google Scholar] [CrossRef]
  61. Li, X.; Cui, H.; Rizzo, J.R.; Wong, E.; Fang, Y. Cross-Safe: A computer vision-based approach to make all intersection-related pedestrian signals accessible for the visually impaired. In Proceedings of the Science and Information Conference, Las Vegas, NV, USA, 25–26 April 2019; Springer: Cham, Switzerland, 2019; pp. 132–146. [Google Scholar]
  62. Ross, R.; Hoque, R. Augmenting GPS with geolocated fiducials to improve accuracy for mobile robot applications. Appl. Sci. 2019, 10, 146. [Google Scholar] [CrossRef] [Green Version]
  63. Palen, L.; Salzman, M.; Youngs, E. Discovery and integration of mobile communications in everyday life. Pers. Ubiquitous Comput. 2001, 5, 109–122. [Google Scholar] [CrossRef]
  64. Dospinescu, O.; Perca, M. Technological integration for increasing the contextual level of information. An. Stiintifice Ale Univ. “Alexandru Ioan Cuza” Din Iasi-Stiinte Econ. 2011, 58, 571–581. [Google Scholar]
  65. Cai, G.S.; Lin, H.Y.; Kao, S.F. Mobile robot localization using gps, imu and visual odometry. In Proceedings of the 2019 International Automatic Control Conference (CACS), Keelung City, Taiwan, 13–16 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–6. [Google Scholar]
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Figure 1. Pedestrian mobility network—dataset within the campus of the Armed Forces University ESPE.
Figure 1. Pedestrian mobility network—dataset within the campus of the Armed Forces University ESPE.
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Figure 2. Web viewer architecture.
Figure 2. Web viewer architecture.
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Figure 3. Registration and variation with initialization topocentric coordinates, EPEC station.
Figure 3. Registration and variation with initialization topocentric coordinates, EPEC station.
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Figure 4. Error vector components, EPEC station. Adapted from implementation of NTRIP technology in mobile browser devices, through an application, in order to obtain GPS coordinates with better precision and in real time [54].
Figure 4. Error vector components, EPEC station. Adapted from implementation of NTRIP technology in mobile browser devices, through an application, in order to obtain GPS coordinates with better precision and in real time [54].
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Figure 5. Application of the error vector for correction of observations of the mobile device. Adapted from implementation of NTRIP technology in mobile browser devices, through an application, to obtain GPS coordinates with better precision and in real time [54].
Figure 5. Application of the error vector for correction of observations of the mobile device. Adapted from implementation of NTRIP technology in mobile browser devices, through an application, to obtain GPS coordinates with better precision and in real time [54].
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Figure 6. Recording of observations in GPS TEST and storage of the NMEA format.
Figure 6. Recording of observations in GPS TEST and storage of the NMEA format.
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Figure 7. Base station and GCP control points.
Figure 7. Base station and GCP control points.
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Figure 8. Variation of static observations—Xiaomi.
Figure 8. Variation of static observations—Xiaomi.
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Figure 9. Distribution of grouped data, horizontal distance—Xiaomi.
Figure 9. Distribution of grouped data, horizontal distance—Xiaomi.
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Figure 10. Variation of static observations—Huawei.
Figure 10. Variation of static observations—Huawei.
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Figure 11. Distribution of grouped data, horizontal distance—Huawei.
Figure 11. Distribution of grouped data, horizontal distance—Huawei.
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Figure 12. Variation of navigation observations—Xiaomi. Red dot is the observation, green rhomb is the corresponding correction.
Figure 12. Variation of navigation observations—Xiaomi. Red dot is the observation, green rhomb is the corresponding correction.
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Figure 13. Variation of navigation observations—Huawei. Red dot is the observation, green rhomb is the corresponding correction.
Figure 13. Variation of navigation observations—Huawei. Red dot is the observation, green rhomb is the corresponding correction.
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Figure 14. Horizontal error magnitude on Xiaomi and Huawei devices.
Figure 14. Horizontal error magnitude on Xiaomi and Huawei devices.
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Table
Table 1. Characteristics of the Xiaomi Poco F3 and Huawei Mate 20 Lite cell phones.
Table 1. Characteristics of the Xiaomi Poco F3 and Huawei Mate 20 Lite cell phones.
CharacteristicsXiaomi Poco F3Huawei Mate 20 Lite
Operating SystemAndroid 11Android 8.1
NavigationGPS: L1 + L5
GLONASS: G1
Galileo: E1 + E5a
BeiDou: B1I + B2a		GPS: L1
GLONASS: G1
Network Capacity2G, 3G, 4G, 5G2G, 3G, 4G
Table
Table 2. Fixed coordinates EPEC-SIRGAS.
Table 2. Fixed coordinates EPEC-SIRGAS.
SIRGAS Weekly Coordinates
CartesianGeographic
X1,277,936.90192 mLength78°26′46.76360″ W
Y−6,251,278.07054 mLatitude00°18′53.60212″ S
Z−34,832.36938 mEllipsoidal Height2522.9743 m
Table
Table 3. Statistical descriptive analysis of static positioning—Xiaomi.
Table 3. Statistical descriptive analysis of static positioning—Xiaomi.
Parameters∆e∆n∆uD. Horizontal
Mean (m)2.714−1.4304.1363.607
Variance (m2)2.5652.96225.3091.926
Std. Dev. (m)±1.602±1.721±5.031±1.388
Maximum (m)5.5962.97916.2116.509
Minimum (m)−0.900−3.998−5.1960.969
Range (m)6.4966.97721.4075.539
C. Asymmetry −0.1980.5760.5350.008
C. Curtosis −0.5040.085−0.045−0.778
Table
Table 4. Statistical descriptive analysis of static positioning—Huawei.
Table 4. Statistical descriptive analysis of static positioning—Huawei.
Parameters∆e∆n∆uD. Horizontal
Mean (m)0.8630.00010.7826.258
Variance (m2)26.17728.436142.24816.191
Std. Dev. (m)±5.116±5.333±11.927±4.024
Maximum (m)11.90616.43224.77919.182
Minimum (m)−12.824−8.013−22.1520.700
Range (m)24.73124.44546.93118.482
C. Asymmetry −0.0840.9440.1500.663
C. Curtosis 0.6041.091−0.340−0.501
Table
Table 5. Statistical descriptive analysis of navigation for variation of the components—Xiaomi.
Table 5. Statistical descriptive analysis of navigation for variation of the components—Xiaomi.
ParametersVariation of
Observed Data		Variation of Corrected Data
∆e∆n∆ec∆nc
Mean (m)−3.8350.504−3.7280.389
Variance (m2)2.86513.2042.92013.268
Std. Dev. (m)±1.693±3.634±1.709±3.643
Maximum (m)−0.4598.681−0.3578.569
Minimum (m)−7.798−7.167−7.772−7.262
Range (m)7.33915.8487.41515.831
Table
Table 6. Statistical descriptive analysis of navigation for horizontal dispersion—Xiaomi.
Table 6. Statistical descriptive analysis of navigation for horizontal dispersion—Xiaomi.
ParametersHorizontal Dispersion
Observed DataCorrected Data
Mean (m)5.1935.103
Variance (m2)4.0614.192
Std. Dev. (m)±2.015±2.047
Maximum (m)10.47510.351
Minimum (m)2.4382.230
Range (m)8.0368.122
C. Variation 0.3880.401
Table
Table 7. Statistical descriptive analysis of navigation for the variation of the components—Huawei.
Table 7. Statistical descriptive analysis of navigation for the variation of the components—Huawei.
ParametersObserved Data VariationCorrected Data Variation
∆e∆n∆ec∆nc
Mean (m)−2.9030.842−2.7960.730
Variance (m2)9.79516.8529.79116.699
Standard Dev. (m)±3.130±4.105±3.129±4.086
Maximum (m)5.7098.4905.8728.363
Minimum (m)−9.054−7.339−8.901−6.960
Range (m)14.76315.82814.77315.323
Table
Table 8. Statistical descriptive analysis of navigation for horizontal dispersion—Huawei.
Table 8. Statistical descriptive analysis of navigation for horizontal dispersion—Huawei.
ParametersHorizontal Dispersion
Observed DataCorrected Data
Mean (m)5.5315.463
Variance (m2)5.1944.992
Standard Dev. (m)±2.279±2.234
Maximum (m)9.2989.106
Minimum (m)0.0340.191
Parameters9.2638.916
C. Variation0.4120.409
Table
Table 9. Horizontal accuracy for static and navigation data.
Table 9. Horizontal accuracy for static and navigation data.
ParametersStaticNavigationCorrection
XiaomiRMSE East3.15134.19204.1006
RMSE North2.23753.66853.6633
RMSE Horizontal3.86485.57055.4986
Accuracy Horizontal6.59509.62019.5019
HuaweiRMSE Este5.18874.26884.1960
RMSE Norte5.33264.19064.1511
RMSE Horizontal7.44035.98205.9024
Accuracy Horizontal12.876410.353110.2157
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Beltrán-Iza, E.A.; Noroña-Meza, C.O.; Robayo-Nieto, A.A.; Padilla, O.; Toulkeridis, T. Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol. Sustainability 2022, 14, 17027. https://doi.org/10.3390/su142417027

AMA Style

Beltrán-Iza EA, Noroña-Meza CO, Robayo-Nieto AA, Padilla O, Toulkeridis T. Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol. Sustainability. 2022; 14(24):17027. https://doi.org/10.3390/su142417027

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Beltrán-Iza, Emilio Alejandro, Cristian Oswaldo Noroña-Meza, Alexander Alfredo Robayo-Nieto, Oswaldo Padilla, and Theofilos Toulkeridis. 2022. "Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol" Sustainability 14, no. 24: 17027. https://doi.org/10.3390/su142417027

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