Navigation Profiles Research Articles

Predicting the chance for being at‐risk of AD based on smartphone data obtained during an everyday wayfinding task in the real world

AbstractBackgroundPatients with Alzheimer’s disease (AD) often show problems in spatial navigation. However, spatial navigation is rarely investigated in real world settings and in people at early disease stages. Capitalizing on recent advancements in digital technologies, we measured the performance of healthy younger (YA) and older adults (OA), as well as older adults with subjective cognitive decline (SCD) while they solved a smartphone‐based wayfinding task.MethodUsing the novel smartphone application “Explore”, we tracked GPS data and other behavioral indicators in 24 YAs, 25 OAs, and 23 SCDs, while they were asked to find points of interest on the medical campus in Magdeburg. In our analyses, we first quantified similarities between the GPS trajectories and identified wayfinding styles using k‐medoids clustering. Next, we extracted navigation profiles with aggregated performance measures as input features (e.g., wayfinding distance and number of help function calls) in a latent profile analysis. Mixed effect models were fitted to evaluate group differences for each performance measure separately. We then fed the performance measure that showed the largest groups differences into a multinomial logistic regression model to predict age and diagnostic group in unknown users via leave‐one‐out cross‐validation.ResultThe GPS data could be clustered into three wayfinding styles. However, the correspondence to our groups was rather low (Figure 1a). In contrast, navigation profiles obtained from the latent profile analysis showed a higher correspondence (Figure 1b). Particularly the number of brief stops during navigation differed between the groups, all p = .006 (Figure 2), and predicted age group, p = .015, and diagnostic group above chance, p = .004 (Figure 3). The leave‐one‐out cross‐validation confirmed the successful prediction of group membership in unknown participants, accuracy = 0.57 (chance level 0.33).ConclusionHere, we show how information about the cognitive health status of an individual can be inferred from smartphone‐data, obtained during a brief episode of a frequently performed everyday behavior. In particular, the number of short stops during wayfinding could be used to distinguish between YA, OA, and SCDs. Hence, real‐world navigation data, obtained from mobile devices, might help to identify individuals at increased risk for developing AD.

Predicting the chance for being at‐risk of AD based on smartphone data obtained during an everyday wayfinding task in the real world

AbstractBackgroundPatients with Alzheimer’s disease (AD) often show problems in spatial navigation. However, spatial navigation is rarely investigated in real world settings and in people at early disease stages. Capitalizing on recent advancements in digital technologies, we measured the performance of healthy younger (YA) and older adults (OA), as well as older adults with subjective cognitive decline (SCD) while they solved a smartphone‐based wayfinding task.MethodUsing the novel smartphone application “Explore”, we tracked GPS data and other behavioral indicators in 24 YAs, 25 OAs, and 23 SCDs, while they were asked to find points of interest on the medical campus in Magdeburg. In our analyses, we first quantified similarities between the GPS trajectories and identified wayfinding styles using k‐medoids clustering. Next, we extracted navigation profiles with aggregated performance measures as input features (e.g., wayfinding distance and number of help function calls) in a latent profile analysis. Mixed effect models were fitted to evaluate group differences for each performance measure separately. We then fed the performance measure that showed the largest groups differences into a multinomial logistic regression model to predict age and diagnostic group in unknown users via leave‐one‐out cross‐validation.ResultThe GPS data could be clustered into three wayfinding styles. However, the correspondence to our groups was rather low (Figure 1a). In contrast, navigation profiles obtained from the latent profile analysis showed a higher correspondence (Figure 1b). Particularly the number of brief stops during navigation differed between the groups, all p ≤ .006 (Figure 2), and predicted age group, p = .015, and diagnostic group above chance, p = .004 (Figure 3). The leave‐one‐out cross‐validation confirmed the successful prediction of group membership in unknown participants, accuracy = 0.57 (chance level 0.33).ConclusionHere, we show how information about the cognitive health status of an individual can be inferred from smartphone‐data, obtained during a brief episode of a frequently performed everyday behavior. In particular, the number of short stops during wayfinding could be used to distinguish between YA, OA, and SCDs. Hence, real‐world navigation data, obtained from mobile devices, might help to identify individuals at increased risk for developing AD.

Fuel-related emission estimation from the navigation profile of the ROKN combat vessel

Fuel-related emission estimation from the navigation profile of the ROKN combat vessel

Self-labelling of tugboat operation using unsupervised machine learning and intensity indicator

The actual operational data, such as a time sequence of fuel consumption and speed, is usually unlabeled or not associated with a specific activity like tugging or cruising. The operation type is critical for further analysis, as tugging and cruising operations require different fuel and navigation profiles. This paper aims to develop a self-labelling framework for tugboat operation by using unsupervised machine learning and a proposed intensity indicator. The framework considers two sets of data: the positional data and the fuel consumption rate data. The fuel consumption data is obtained from mass flowmeters installed on tugboats, while the positional data are navigational data purchased from marine data aggregators. The developed self-labelling enables ship operators in identifying operations and locations that require heavy fuel consumption andcan be used for further big data analytics and machine learning for fuel consumption prediction when vessel speeds are known.

The role of relevance determinations in multiple text reading and writing: an investigation of the MD-TRACE

ABSTRACT This study examined the role of relevance determinations within the context of undergraduates’ multiple text reading and writing. In this study, undergraduate students were randomly assigned to one of two experimental conditions (i.e., to compose a research report about either the causes of or the solutions to the urban housing crisis), using a library of 12 digital texts (six more relevant and six less relevant to students’ assigned task condition). Guided by the Multiple Documents Task-Based Relevance Assessment and Content Extraction (MD-TRACE) model, we identified the key features that students included in their task models, used log data to profile students’ text selection justifications and navigation, and categorized students’ writing as task-relevant or not. As such, we found students’ relevance determinations to play a key role in forming task models prior to text access, selecting and navigating texts during multiple text use, and composing a task product after accessing multiple texts. While we did not find students’ initial task models to be associated with their patterns of text selection justifications or navigation nor with writing performance, we did find differences in writing performance across students belonging to different text selection justification and navigation profiles. Implications for theory and research on learning from multiple texts are discussed.

Jóvenes y tecnologías digitales. Diagnóstico del uso y apropiación de plataformas digitales en la zona conurbada del sur de Tamaulipas

En México, existen diversos estudios sobre qué hacen los jóvenes en la red y cómo interactúan en estos entornos; no obstante, en lo referente al sur de Tamaulipas, no existen investigaciones específicas que aborden las características de estas prácticas. El objetivo de este trabajo fue diagnosticar el uso y apropiaciones de plataformas digitales entre jóvenes de la zona sur de Tamaulipas, México, en su calidad de estudiantes del nivel medio superior y superior. La metodología es cuantitativa, a partir de un diseño no-experimental descriptivo. Se diseñó una muestra estadísticamente representativa de los municipios de Tampico, Ciudad Madero y Altamira, con un 95 % de nivel de confianza; a partir de la que se aplicó un cuestionario que obtuvo la participación de 1 222 jóvenes, en el que, se evaluó la intensidad de uso y perfil de navegación de diversas plataformas digitales, como páginas de información general, de intercambio de información o productos, redes sociales digitales, páginas educativas y de aprendizaje, así como de entretenimiento y diversión. Los hallazgos muestran que los procesos de uso y apropiación de estos entornos están mediados por 2 elementos fundamentales: un componente técnico, referido a los recursos de interactividad que provea la propia plataforma; y un componente sociocultural que remite a la historia de vida, el capital social y cultural, la personalidad y capacidad expresiva, así como las habilidades digitales de los jóvenes. Este estudio, desde lo local, permite observar la homogeneidad en el uso y apropiación de las plataformas digitales de los jóvenes, al tener un sentido pasivo en cuanto al empoderamiento a través de estas herramientas, para aprovechar los potenciales identificados de habilidades digitales, colaboración y producción de contenidos.

Vertical flight profile optimization for a cruise segment with RTA constraints

ABSTRACTThis paper presents the results of a research performed at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticty (LARCASE), at ÉTS, concerning optimisation strategies for cruise flight segments with imposed flight time (delimited by waypoints with required time of arrival constraints). Specifically, a new algorithm is presented that identifies the optimal vertical navigation profile (flight altitude and speed optimisation) for a cruise segment with imposed lateral navigation profile, bounded by two waypoints with required time of arrival constraints. The set of evaluated vertical navigation profiles are characterised by identical altitudes and speeds at their initial and final waypoints (at the beginning and the end of the cruise segment under optimisation), a maximum of one altitude step (relative to the initial altitude), and are flown at constant speed. This study investigates the flight performance increase (total cost reduction) for a flight along the optimal vertical navigation profile, relative to a flight at the optimal speed and initial cruise altitude. The evaluation was performed using a medium haul transport aircraft flight performance model, for three lateral navigation profiles and three wind profiles. The algorithm is targeted for Flight Management Systems platforms, to provide the optimal flight trajectory for the imposed lateral flight profile and time constraints.

Aircraft Vertical Route Optimization by Beam Search and Initial Search Space Reduction

This paper describes an optimization algorithm that provides an economical vertical navigation profile by finding the combinations of climb, cruise, and descent speeds, as well as altitudes, for an aircraft to minimize flight costs. The computational algorithm takes advantage of a space search reduction methodology to reduce the initial number of available speed and altitude combinations. The optimal solution was found by implementing the beam search algorithm. A bounding function that correctly estimates the flight cost by considering step climbs was developed to reduce the number of calculations required by the beam search algorithm. The full-flight fuel burn cost was obtained using a performance database-based method. The algorithm uses a numerical performance model instead of equations of motion to compute fuel burn. The database was developed by using flight experimental data. To validate the algorithm, its results were compared to those of three other algorithms: an exhaustive search, beam search, and search space reduction. The solution provided by the algorithm was also compared to the solution provided by a flight management system. Following this comparison, the algorithm systematically found the optimal solutions, which were better in terms of flight cost than those provided by the flight management system.

Text navigation in multiple source use

Drawing on theories in evolutionary biology, research on hypertext navigation has posited two profiles to capture how students navigate information sources: the satisficing and sampling approaches of text access. While students engaged in sampling work to identify an optimal source to exploit for information, students who adopt a satisficing approach to text use spend time on accessing the first text they visit that meets some threshold of acceptability. This study examines the manifestation of these profiles when students navigate multiple, non-hyperlinked texts, without time limitations. Evidence was found for a satisficing, but not a sampling, approach to multiple text navigation. Four sub-profiles of satisficing approaches were identified. Students in the limited navigation profile devoted little time to text access. Students in the primary profile devoted the bulk of access time to a single text. Those in the distributed profile visited the texts they accessed for fairly uniform periods of time. Students in the discriminating profile visited certain texts for substantial periods of time, while accessing other texts to a more limited extent. These four navigation profiles were found to be differentially associated with other metrics of text access (e.g., whether texts were revisited), ratings of text usefulness, and task performance.

Veering in hemi-Parkinson’s disease: Primacy of visual over motor contributions

Veering while walking is often reported in individuals with Parkinson’s disease (PD), with potential mechanisms being vision-based (asymmetrical perception of the visual environment) or motoric (asymmetry in stride length between relatively affected and non-affected body side). We examined these competing hypotheses by assessing veering in 13 normal control participants (NC) and 20 non-demented individuals with PD: 9 with left-side onset of motor symptoms (LPD) and 11 with right-side onset (RPD). Participants walked in a corridor under three conditions: eyes-open, egocentric reference point (ECRP; walk toward a subjectively perceived center of a target at the end of the corridor), and vision-occluded. The visual hypothesis predicted that LPD participants would veer rightward, in line with their rightward visual-field bias, whereas those with RPD would veer leftward. The motor hypothesis predicted the opposite pattern of results, with veering toward the side with shorter stride length. Results supported the visual hypothesis. Under visual guidance, RPD participants significantly differed from NC, veering leftward despite a shorter right- than left-stride length, whereas LPD veered rightward (not significantly different from NC), despite shorter left- than right-stride length. LPD participants showed significantly reduced rightward veering and stride asymmetry when they walked in the presence of a visual landmark (ECRP) than in the eyes-open condition without a target. There were no group differences in veering in the vision-occluded condition. The findings suggest that interventions to correct walking abnormalities such as veering in PD should incorporate vision-based strategies rather than solely addressing motor asymmetries, and should be tailored to the distinctive navigational profiles of LPD and RPD.

Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation

For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.

Recommendation Engine Formation Using Depth First Search and Genetic Approach

The requirement of online users in the website varies dynamically. The recommendation of web pages consisting of user expected information and data is performed by the online recommendation system. The recommendation engine must be self-adaptive and accurate. The existing algorithm uses Depth First Search (DFS) and bee’s foraging approach to create navigation profiles by categorizing the current user activity. The prediction of navigations that are most expected to be visited by online users is also performed. In this study, the recommendation engine formation with optimized resource such as memory, CPU usage and minimum time consumption is proposed using DFS and Genetic Approach (GA). Here, initially the cluster formation is achieved using DFS approach. The method creates an eminent browsing pattern for each user using live session window. The performance of the approach is compared with the existing forager agent. The experimental results show that the proposed approach outperforms the existing methods in accomplishing accurate classification and anticipation of future navigation for the current online user.

Fuel burn and emissions evaluation for a missed approach procedure performed by a B737-400

AbstractThis paper presents the results of a study conducted at the Laboratoire de recherche en commande active, avionique et aéroservoélasticité, École de technologie supérieure, regarding the costs in fuel burn and pollutant emissions corresponding to a flight profile determined by a missed approach procedure. The evaluated missed approach flight profile starts at the point where the missed approach decision is made and ends at the same point, after the aircraft completes the missed approach procedure.This study uses the fuel burn and pollutant emissions data for a Boeing 737-400 aircraft (chosen at random for this study), published by the European Environment Agency, and the standard flying cycle model elaborated by Group 08 of the Task Force on Emissions Inventories and Projections – a task force created by the United Nations Economic Commission for Europe (UN ECE)/the co-operative program for the monitoring and evaluation of the long-range transmission of air pollutants in the Europe (EMEP). The missed approach fight profile is based on the Area Navigation/Required Navigation Performance procedure for runway 13R of the King County International Airport/Boeing Field (BFI) in Seattle.Four study cases are considered for the missed approach flight evaluation. These cases correspond to four navigation profiles – a combination of two lateral navigation profiles, one with a 20 nautical miles holding pattern and the other without, and two vertical navigation profiles. The results obtained for the missed approach flight profiles, in terms of fuel burn and emissions, are compared with the fuel burn and emissions reference data corresponding to a standard approach procedure and complete flights (from taxi out to taxi in).

Improve Efficiency of Web Pattern Analysis Through Web Mining

In commercial and academic areas of Web usage mining techniques, the interest has been increased due to the information explosion in World Wide Web. Through web user navigation patterns, proposed a approach which predict users’ future request that provide valuable information for web designers which quickly respond to their individual needs for the efficient organization of the website by mining the weblog files using Web weblog Expert and Universal Extractor. The approach is based on the combined mining to discover knowledge from web server log files which derive user navigation profiles and the contents of the retrieved web pages. The proposed algorithm implemented, and the system achieves the bandwidth of nearly 80% and the efficiency of about 75% of extracted log files, which is about 20% higher than original log files by mining Web server logs. This approach has been carried out in order to validate and facilitate better web personalization and website organization.

Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it

The tourism industry has experienced a shift from offline to online travellers and this has made the use of intelligent systems in the tourism sector crucial. These information systems should provide tourism consumers and service providers with the most relevant information, more decision support, greater mobility and the most enjoyable travel experiences. As a consequence, Destination Marketing Organizations (DMOs) not only have to respond by adopting new technologies, but also by interpreting and using the knowledge created by the use of these techniques. This work presents the design of a general and non-invasive web mining system, built using the minimum information stored in a web server (the content of the website and the information from the log files stored in Common Log Format (CLF)) and its application to the Bidasoa Turismo (BTw) website. The proposed system combines web usage and content mining techniques with the three following main objectives: generating user navigation profiles to be used for link prediction; enriching the profiles with semantic information to diversify them, which provides the DMO with a tool to introduce links that will match the users taste; and moreover, obtaining global and language-dependent user interest profiles, which provides the DMO staff with important information for future web designs, and allows them to design future marketing campaigns for specific targets. The system performed successfully, obtaining profiles which fit in more than 60% of cases with the real user navigation sequences and in more than 90% of cases with the user interests. Moreover the automatically extracted semantic structure of the website and the interest profiles were validated by the BTw DMO staff, who found the knowledge provided to be very useful for the future.

On the Construction of a Skill-Based Wheelchair Navigation Profile

Assisted wheelchair navigation is of key importance for persons with severe disabilities. The problem has been solved in different ways, usually based on the shared control paradigm. This paradigm consists of giving the user more or less control on a need basis. Naturally, these approaches require personalization: each wheelchair user has different skills and needs and it is hard to know a priori from diagnosis how much assistance must be provided. Furthermore, since there is no such thing as an average user, sometimes it is difficult to quantify the benefits of these systems. This paper proposes a new method to extract a prototype user profile using real traces based on more than 70 volunteers presenting different physical and cognitive skills. These traces are clustered to determine the average behavior that can be expected from a wheelchair user in order to cope with significant situations. Processed traces provide a prototype user model for comparison purposes, plus a simple method to obtain without supervision a skill-based navigation profile for any user while he/she is driving. This profile is useful for benchmarking but also to determine the situations in which a given user might require more assistance after evaluating how well he/she compares to the benchmark. Profile-based shared control has been successfully tested by 18 volunteers affected by left or right brain stroke at Fondazione Santa Lucia, in Rome, Italy.

Ontology driven bee's foraging approach based self adaptive online recommendation system

Online recommendation system is the modern software system used in all the e-commerce sites to capture the user intent and recommend the web pages that contain user expected information. The important challenges for such a system must include a need of being self-adaptive because the needs for online users may change dynamically. Classifier plays a very important role to improve the overall system accuracy. Here, we proposed the Ontology driven bee's foraging approach (ODBFA) that accurately classify the current user activity to any of the navigation profiles and predict the navigations that most likely to be visited by online users.Our proposed ODBFA method uses the Honey bee foraging behaviour in selecting the more profitable navigation profile for the current user activity. This approach makes the system self adaptive by capturing the changing needs of online user with the help of ontological framework comprising of ontology based similarity comparison and scoring algorithm. This approach effectively outperforms the other methods in achieving accurate classification and prediction of future navigation for the current online user.

IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

Problem statement: Recently, Web usage mining techniques have been widely used to build recommendation systems especially for anonymous users. Approach: Assigning the current user to the best web navigation profile with similar navigation activities will improve the ability of the prediction engine to produce a recommendation list then introduce it to the user. This study presents iPACT an improved recommendation system using Profile Aggregation based on Clustering of Transactions (PACT). Results: iPACT shows better prediction accuracy than the previous methods PACT and Hypergraph. Conclusion: The users’ interests change over time; hence an incremental and adaptive web navigation profiling is a key feature for the future works.

Mining Web Navigation Profiles For Recommendation System

This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the next web navigations. Most of the recommendation systems are user-centered which make a prediction list to the users based on their long term navigation history, user’s databases or full user’s profiles. Companies wish to attract anonymous users, directed them at the early stages of their visits and get them involved with their websites. Learning and mining the web navigation profiles followed by enhanced classification to the similar activities of previous users will provide an appropriate model to recommend to the current anonymous active user with short term navigation. Using CTI dataset, the experimental results show better prediction accuracy than the previous works. An adaptive profiling to save time is a key factor for future works.

Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests

We present a study of the automatic classification of web user navigation patterns and propose a novel approach to classifying user navigation patterns and predicting users’ future requests. The approach is based on the combined mining of Web server logs and the contents of the retrieved web pages. The textual content of web pages is captured through extraction of character N-grams, which are combined with Web server log files to derive user navigation profiles. The approach is implemented as an experimental system, and its performance is evaluated based on two tasks: classification and prediction. The system achieves the classification accuracy of nearly 70% and the prediction accuracy of about 65%, which is about 20% higher than the classification accuracy by mining Web server logs alone. This approach may be used to facilitate better web personalization and website organization.