Smartphone-based User Location Tracking in Indoor Environment

International audienceThis paper introduces our work in the framework of Track 3 of the IPIN 2016 Indoor Localization Competition, which addresses the smartphone-based tracking problem in an offline manner.Our approach splits the path-reconstruction into several smaller tasks, including building identification, floor identification, user direction and speed inference.For each task, a specific set of data from the provided log data is used.Evaluation is carried out using a cross validation scheme.To produce the robustness again noisy data, we combine several approaches into one on the basis of their testing results.By testing on the provided training data, we have a good accuracy on building and floor identification. For the task of tracking the user's position within the floor, the result is 10m at 3rd-quarter distance error after 3 minutes of walking

Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth

In a multiuser context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the user's position directly. However, both the Wi-Fi-based position outputs and Bluetooth-based distances are affected by some degree of noise. In our work, we propose several approaches to combine the two types of outputs for improving the tracking accuracy in the context of collaborative positioning. The two proposed approaches attempt to build a model for measuring the errors of the Bluetooth output and Wi-Fi output. In a non-temporal approach, the model establishes the relationship in a specific interval of the Bluetooth output and Wi-Fi output. In a temporal approach, the error measurement model is expanded to include the time component between users' movement. To evaluate the performance of the two approaches, we collected the data from several multiuser scenarios in indoor environment. The results show that the proposed approaches could reach a distance error around 3.0m for 75 percent of time, which outperforms the positioning results of the standard Wi-Fi fingerprinting model.Comment: International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sep 2018, Nantes, Franc

The Grenoble System for the Social Touch Challenge at ICMI 2015

International audienceNew technologies and especially robotics is going towards more natural user interfaces. Works have been done in different modality of interaction such as sight (visual computing), and audio (speech and audio recognition) but some other modalities are still less researched. The touch modality is one of the less studied in HRI but could be valuable for naturalistic interaction. However touch signals can vary in semantics. It is therefore necessary to be able to recognize touch gestures in order to make human-robot interaction even more natural.We propose a method to recognize touch gestures. This method was developed on the CoST corpus and then directly applied on the HAART dataset as a participation of the Social Touch Challenge at ICMI 2015.Our touch gesture recognition process is detailed in this article to make it reproducible by other research teams.Besides features set description, we manually filtered the training corpus to produce 2 datasets.For the challenge, we submitted 6 different systems.A Support Vector Machine and a Random Forest classifiers for the HAART dataset.For the CoST dataset, the same classifiers are tested in two conditions: using all or filtered training datasets.As reported by organizers, our systems have the best correct rate in this year's challenge (70.91% on HAART, 61.34% on CoST).Our performances are slightly better that other participants but stay under previous reported state-of-the-art results

Infection with high-risk HPV types among female sex workers in northern Vietnam

Vaccines against two high-risk human papillomavirus (HPV) types, HPV-16, and HPV-18, are in use currently, with high efficacy for preventing infections with these HPV types and consequent cervical cancers. However, circulating HPV types can vary with geography and ethnicity. The aim of this study was to investigate the prevalence of HPV types and the association between HPV types and abnormal cervical cytology among female sex workers in Northern Vietnam. Cervical swabs and plasma samples were collected from 281 female sex workers at two health centers in Hanoi and Hai Phong in 2009. The HPV L1 gene was amplified by PCR using original and modified GP5+/6+ primers. Amplified PCR products were genotyped by the microarray system GeneSquare (KURABO) and/or clonal sequencing. Of the 281 women, 139 (49.5%) were positive for HPV DNA. Among the HPV-positive samples, 339 strains and 29 different types were identified. Multiple-type and high risk-type HPV infections were found in 85 (61.2%) and 124 (89.2%) women, respectively. The most common genotype was HPV-52, followed by HPV-16, HPV-18, and HPV-58. Abnormal cervical cytology was detected in 3.2% (9/281) of the women, and all of these samples were positive for HPV-DNA. Age ≤25 years and infection with human immunodeficiency virus were associated positively with HPV infection among the women while ever smoking was associated negatively. These results show that HPV-52 is most prevalent among female sex workers in Northern Vietnam, most of whom had normal cervical cytology. This information may be important for designing vaccination strategies in Vietnam. J. Med. Virol. 85:288-294, 2013. © 2012 Wiley Periodicals, Inc. Copyright © 2012 Wiley Periodicals, Inc

The smartphone-based offline indoor location competition at IPIN 2016: analysis and future work

This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.We would like to thank Tecnalia Research & Innovation Foundation for sponsoring the competition track with an award for the winning team. We are also grateful to Francesco Potortì, Sangjoon Park, Jesús Ureña and Kyle O’Keefe for their invaluable help in promoting the IPIN competition and conference. Parts of this work was carried out with the financial support received from projects and grants: LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R (MINECO/FEDER)), SmartLoc (CSIC-PIE Ref.201450E011), “Metodologías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores” (TIN2015-70202-P), REPNIN network (TEC2015-71426-REDT) and the José Castillejo mobility grant (CAS16/00072). The HFTS team has been supported in the frame of the German Federal Ministry of Education and Research programme “FHprofUnt2013” under contract 03FH035PB3 (Project SPIRIT). The UMinho team has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

E6 and E7 variants of human papillomavirus-16 and -52 in Japan, the Philippines, and Vietnam

Human papillomavirus (HPV) has several intragenotypic variants with different geographical and ethnic distributions. This study aimed to elucidate the distribution patterns of E6 and E7 (E6/E7) intragenotypic variants of HPV type 16 (HPV-16), which is most common worldwide, and HPV-52, which is common in Asian countries such as Japan, the Philippines, and Vietnam. In previous studies, genomic DNA samples extracted from cervical swabs were collected from female sex workers in these three countries and found to be positive for HPV-16 or HPV-52. Samples were amplified further for their E6/E7 genes using type-specific primers and analyzed genetically. Seventy-nine HPV-16 E6/E7 genes were analyzed successfully and grouped into three lineages: European (Prototype), European (Asian), and African-2. The prevalences of HPV-16 European (Prototype)/European (Asian) lineages were 19.4%/80.6% (n=31) in Japan, 75.0%/20.8% (n=24) in the Philippines, and 0%/95.8% (n=24) in Vietnam. The 109 HPV-52 E6/E7 genes analyzed successfully were grouped into four lineages, A-D; the prevalences of lineages A/B/C/D were, respectively, 5.1%/92.3%/0%/2.6% in Japan (n=39), 34.4%/62.5%/0%/3.1% in the Philippines (n=32), and 15.8%/73.7%/7.9%/2.6% in Vietnam (n=38). The distribution patterns of HPV-16 and HPV-52 lineages in these countries differed significantly (P<0.000001 and P=0.0048, respectively). There was no significant relationship between abnormal cervical cytology and either HPV-16 E6/E7 lineages or specific amino acid mutations, such as E6 D25E, E6 L83V, and E7 N29S. Analysis of HPV-16 and HPV-52 E6/E7 genes can be a useful molecular-epidemiological tool to distinguish geographical diffusion routes of these HPV types in Asia. © 2013 Wiley Periodicals, Inc

Positionnement intérieur basé sur les smartphones à l'aide de Wi-Fi, des capteurs inertiels et Bluetooth

With the popularity of smartphones and tablets in daily life, the task of finding user’s position through their phone gains much attention from both the research and industry communities. Technologies integrated in smartphones such as GPS, Wi-Fi, Bluetooth and camera are all capable for building a positioning system. Among those technologies, GPS has approaches have become a standard and achieved much success for the outdoor environment. Meanwhile, Wi-Fi, inertial sensors and Bluetooth are more preferred for positioning task in indoor environment.For smartphone positioning, Wi-Fi fingerprinting based approaches are well established within the field. Generally speaking, the approaches attempt to learn the mapping function from Wi-Fi signal characteristics to the real world position. They usually require a good amount of data for finding a good mapping. When the available training data is limited, the fingerprinting-based approach has high errors and becomes less stable. In our works, we want to explore different approaches of Wi-Fi fingerprinting methods for dealing with a lacking in training data. Based on the performance of the individual approaches, several ensemble strategies are proposed to improve the overall positioning performance. All the proposed methods are tested against a published dataset, which is used as the competition data of the IPIN 2016 Conference with offsite track (track 3).Besides the positioning system based on Wi-Fi technology, the smartphone’s inertial sensors are also useful for the tracking task. The three types of sensors, which are accelerate, gyroscope and magnetic, can be employed to create a Step-And-Heading (SHS) system. Several methods are tested in our approaches. The number of steps and user’s moving distance are calculated from the accelerometer data. The user’s heading is calculated from the three types of data with three methods, including rotation matrix, Complimentary Filter and Madgwick Filter. It is reasonable to combine SHS outputs with the outputs from Wi-Fi due to both technologies are present in the smartphone. Two combination approaches are tested. The first approach is to use directly the Wi-Fi outputs as pivot points for fixing the SHS tracking part. In the second approach, we rely on the Wi-Fi signal to build an observation model, which is then integrated into the particle filter approximation step. The combining paths have a significant improvement from the SHS tracking only and the Wi-Fi only. Although, SHS tracking with Wi-Fi fingerprinting improvement achieves promising results, it has a number of limitations such as requiring additional sensors calibration efforts and restriction on smartphone handling positions.In the context of multiple users, Bluetooth technology on the smartphone could provide the approximated distance between users. The relative distance is calculated from the Bluetooth inquiry process. It is then used to improve the output from Wi-Fi positioning models. We study two different combination methods. The first method aims to build an error function which is possible to model the noise in the Wi-Fi output and Bluetooth approximated distance for each specific time interval. It ignores the temporal relationship between successive Wi-Fi outputs. Position adjustments are then computed by minimizing the error function. The second method considers the temporal relationship and the movement constraint when the user moves around the area. The tracking step are carried out by using particle filter. The observation model of the particle filter are a combination between the Wi-Fi data and Bluetooth data. Both approaches are tested against real data, which include up to four different users moving in an office environment. While the first approach is only applicable in some specific scenarios, the second approach has a significant improvement from the position output based on Wi-Fi fingerprinting model only.Grâce à l’émergence dans la vie quotidienne des appareils de plus en plus populaires que sont les smartphones et les tablettes, la tâche de postionner l'utilisateur par le biais de son téléphone est une problématique fortement étudiée dans les domaines non seulement de la recherche mais également des communautés industrielles. Parmi ces technologies, les approches GPS sont devenues une norme et ont beaucoup de succès pour une localisation en environnement extérieur. Par contre, le Wi-Fi, les capteurs inertiels et le Bluetooth sont plutôt préférés pour les tâches de positionnement dans un environnement intérieur.Pour ce qui concerne le positionnement des smartphones, les approches basées sur les « empreintes digitales » (fingerprint) Wi-Fi sont bien établies. D'une manière générale, ces approches tentent d'apprendre la fonction de correspondance (cartographie) des caractéristiques du signal Wi-Fi par rapport à la position de l’appareil dans le monde réel. Elles nécessitent généralement une grande quantité de données pour obtenir une bonne cartographie. Lorsque ces données d'entraînement disponibles sont limitées, l'approche basée sur les empreintes digitales montre alors des taux d’erreurs élevés et devient moins stable. Dans nos travaux, nous explorons d’autres approches, différentes, pour faire face à cette problématique du manque de données d'entraînement. Toutes ces méthodes sont testées sur un ensemble de données public qui est utilisé lors d’une compétition internationale à la Conférence IPIN 2016.En plus du système de positionnement basé sur la technologie Wi-Fi, les capteurs inertiels du smartphone sont également utiles pour la tâche de suivi. Les trois types de capteurs, qui sont les accéléromètres, le gyroscope et la boussole magnétique, peuvent être utilisés pour suivre l'étape et la direction de l'utilisateur (méthode SHS). Le nombre d'étapes et la distance de déplacement de l'utilisateur sont calculés en utilisant les données de l'accéléromètre. La position de l'utilisateur est calculée par trois types de données avec trois méthodes comprenant la matrice de rotation, le filtre complémentaire et le filtre de Madgwick. Il est raisonnable de combiner les sorties SHS avec les sorties de Wi-Fi, car les deux technologies sont présentes dans les smartphones et se complètent. Deux approches combinées sont testées. La première approche consiste à utiliser directement les sorties Wi-Fi comme points de pivot pour la fixation de la partie de suivi SHS. Dans la deuxième approche, nous comptons sur le signal Wi-Fi pour construire un modèle d'observation, qui est ensuite intégré à l'étape d'approximation du filtre à particules. Ces combinaisons montrent une amélioration significative par rapport au suivi SHS ou au suivi Wi-Fi uniquement.Dans un contexte multiutilisateur, la technologie Bluetooth du smartphone pourrait fournir une distance approximative entre les utilisateurs. La distance relative est calculée à partir du processus de numérisation du périphérique Bluetooth. Elle est ensuite utilisée pour améliorer la sortie des modèles de positionnement Wi-Fi. Nous étudions deux méthodes. La première vise à créer une fonction d'erreur qui permet de modéliser le bruit dans la sortie Wi-Fi et la distance approximative produite par le Bluetooth pour chaque intervalle de temps spécifié. La seconde méthode considère par contre cette relation temporelle et la contrainte de mouvement lorsque l'utilisateur se déplace. Le modèle d'observation du filtre à particules est une combinaison entre les données Wi-Fi et les données Bluetooth. Les deux approches sont testées en fonction de données réelles, qui incluent jusqu'à quatre utilisateurs différents qui se déplacent dans un bureau. Alors que la première approche n'est applicable que dans certains scénarios spécifiques, la deuxième approche montre une amélioration significative par rapport aux résultats de position basés uniquement sur le modèle d'empreintes digitales Wi-Fi

Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth

International audienceIn a multiuser context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the user's position directly. However, both the Wi-Fi-based position outputs and Bluetooth-based distances are affected by some degree of noise. In our work, we propose several approaches to combine the two types of outputs for improving the tracking accuracy in the context of collaborative positioning. The two proposed approaches attempt to build a model for measuring the errors of the Bluetooth output and Wi-Fi output. In a non-temporal approach, the model establishes the relationship in a specific interval of the Bluetooth output and Wi-Fi output. In a temporal approach, the error measurement model is expanded to include the time component between users' movement. To evaluate the performance of the two approaches, we collected the data from several multiuser scenarios in indoor environment. The results show that the proposed approaches could reach a distance error around 3.0m for 75 percent of time, which outperforms the positioning results of the standard Wi-Fi fingerprinting model

Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies

International audienceFor the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by Bluetooth data are subject to a certain degree of noise due to the uncertainty of radio propagation in complex indoor environments. In this study, we propose and evaluate two approaches, namely Non-temporal and Temporal ones, of collaborative positioning to combine these two cohabiting technologies to improve the tracking performance. In the Non-temporal approach, our model establishes an error observation function in a specific interval of the Bluetooth and Wi-Fi output. It is then able to reduce the positioning error by looking for ways to minimize the error function. The Temporal approach employs an extended error model that takes into account the time component between users’ movements. For performance evaluation, several multi-user scenarios in an indoor environment are set up. Results show that for certain scenarios, the proposed approaches attain over 40% of improvement in terms of average accuracy

Determinants of Farming Households’ Credit Accessibility in Rural Areas of Vietnam: A Case Study in Haiphong City, Vietnam

The role of agricultural sectors in the economic development of a country is undeniable, especially in developing and least-developed ones, ensuring food supply, increasing national income, export earnings and poverty reduction. Vietnam is known as an emerging market, depending directly on agriculture-related activities for their livelihood, in which the issue of rural credit access still remains a confounding problem. The paper focuses on identifying the determinants of credit access in rural areas of Vietnam using Haiphong city as a case study, including formal and informal credit. The paper uses data collected from a survey of 180 rural households in a district of Haiphong city. The probit and linear regression models are applied to investigate the factors that determine household credit accessibility, i.e., the household’s decision to borrow and borrowing amounts. Results of this analysis reveal the different significant determinants of formal and informal credit market access. Group membership and connection are found to have significantly strong impacts on formal credit accessibility while informal credit access is strongly influenced by agriculture income and dependency ratio. The implications of these findings for enhancing formal credit accessibility and decreasing the dependence on informal markets are discussed