Pub Date : 2017-05-04DOI: 10.1109/PERCOMW.2017.7917580
L. Schumacher, Marie-Ange Remiche
Thanks to the fact that Belgium is a densely populated country, and also lags behind in the roll-out of Long Term Evolution (LTE) / 4G, it is still possible to visit areas enjoying different Radio Access Technology (RAT) coverages within a limited territory. This paper reports an analysis of the dataset of web requests collected through a field survey mostly performed in south-western Belgium and northern France. This analysis focuses on the impact of the spreading of web sites across CDNs.
{"title":"Sensitivity to web hosting in a mobile field survey","authors":"L. Schumacher, Marie-Ange Remiche","doi":"10.1109/PERCOMW.2017.7917580","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917580","url":null,"abstract":"Thanks to the fact that Belgium is a densely populated country, and also lags behind in the roll-out of Long Term Evolution (LTE) / 4G, it is still possible to visit areas enjoying different Radio Access Technology (RAT) coverages within a limited territory. This paper reports an analysis of the dataset of web requests collected through a field survey mostly performed in south-western Belgium and northern France. This analysis focuses on the impact of the spreading of web sites across CDNs.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115367693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-04DOI: 10.1109/PERCOMW.2017.7917548
J. Rafferty, J. Synnott, C. Nugent, Gareth Morrison, E. Tamburini
Pervasive and ubiquitous computing increasingly relies on data-driven models learnt from large datasets. This learning process requires annotations in conjunction with datasets to prepare training data. Ambient Assistive Living (AAL) is one application of pervasive and ubiquitous computing that focuses on providing support for individuals. A subset of AAL solutions exist which model and recognize activities/behaviors to provide assistive services. This paper introduces an annotation mechanism for an AAL platform that can recognize, and provide alerts for, generic activities/behaviors. Previous annotation approaches have several limitations that make them unsuited for use in this platform. To address these deficiencies, an annotation solution relying on environmental NFC tags and smartphones has been devised. This paper details this annotation mechanism, its incorporation into the AAL platform and presents an evaluation focused on the efficacy of annotations produced. In this evaluation, the annotation mechanism was shown to offer reliable, low effort, secure and accurate annotations that are appropriate for learning user behaviors from datasets produced by this platform. Some weaknesses of this annotation approach were identified with solutions proposed within future work.
{"title":"NFC based dataset annotation within a behavioral alerting platform","authors":"J. Rafferty, J. Synnott, C. Nugent, Gareth Morrison, E. Tamburini","doi":"10.1109/PERCOMW.2017.7917548","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917548","url":null,"abstract":"Pervasive and ubiquitous computing increasingly relies on data-driven models learnt from large datasets. This learning process requires annotations in conjunction with datasets to prepare training data. Ambient Assistive Living (AAL) is one application of pervasive and ubiquitous computing that focuses on providing support for individuals. A subset of AAL solutions exist which model and recognize activities/behaviors to provide assistive services. This paper introduces an annotation mechanism for an AAL platform that can recognize, and provide alerts for, generic activities/behaviors. Previous annotation approaches have several limitations that make them unsuited for use in this platform. To address these deficiencies, an annotation solution relying on environmental NFC tags and smartphones has been devised. This paper details this annotation mechanism, its incorporation into the AAL platform and presents an evaluation focused on the efficacy of annotations produced. In this evaluation, the annotation mechanism was shown to offer reliable, low effort, secure and accurate annotations that are appropriate for learning user behaviors from datasets produced by this platform. Some weaknesses of this annotation approach were identified with solutions proposed within future work.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122616985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-02DOI: 10.1109/PERCOMW.2017.7917561
Fatjon Seraj, N. Meratnia, P. Havinga
Smartphones have revolutionized the way infrastructure health monitoring applications operate. Their ubiquitous sensing and communication capabilities have made measurement data for infrastructural health monitoring applications easily available. They, however, also introduced a new challenge, namely the huge amount of data that is generated. This new reality prompts the need for efficient techniques to handle, process, aggregate, and visualize this huge amount of streaming data.
{"title":"An aggregation and visualization technique for crowd-sourced continuous monitoring of transport infrastructures","authors":"Fatjon Seraj, N. Meratnia, P. Havinga","doi":"10.1109/PERCOMW.2017.7917561","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917561","url":null,"abstract":"Smartphones have revolutionized the way infrastructure health monitoring applications operate. Their ubiquitous sensing and communication capabilities have made measurement data for infrastructural health monitoring applications easily available. They, however, also introduced a new challenge, namely the huge amount of data that is generated. This new reality prompts the need for efficient techniques to handle, process, aggregate, and visualize this huge amount of streaming data.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121721208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917543
Fausto Giunchiglia, Enrico Bignotti, M. Zeni
Context is a fundamental tool humans use for understanding their environment, and it must be modelled in a way that accounts for the complexity faced in the real world. Current context modelling approaches mostly focus on a priori defined environments, while the majority of human life is in open, and hence complex and unpredictable, environments. We propose a context model where the context is organized according to the different dimensions of the user environment. In addition, we propose the notions of endurants and perdurants as a way to describe how humans aggregate their context depending either on space or time, respectively. To ground our modelling approach in the reality of users, we collaborate with sociology experts in an internal university project aiming at understanding how behavioral patterns of university students in their everyday life affect their academic performance. Our contribution is a methodology for developing annotations general enough to account for human life in open domains and to be consistent with both sensor data and sociological approaches.
{"title":"Personal context modelling and annotation","authors":"Fausto Giunchiglia, Enrico Bignotti, M. Zeni","doi":"10.1109/PERCOMW.2017.7917543","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917543","url":null,"abstract":"Context is a fundamental tool humans use for understanding their environment, and it must be modelled in a way that accounts for the complexity faced in the real world. Current context modelling approaches mostly focus on a priori defined environments, while the majority of human life is in open, and hence complex and unpredictable, environments. We propose a context model where the context is organized according to the different dimensions of the user environment. In addition, we propose the notions of endurants and perdurants as a way to describe how humans aggregate their context depending either on space or time, respectively. To ground our modelling approach in the reality of users, we collaborate with sociology experts in an internal university project aiming at understanding how behavioral patterns of university students in their everyday life affect their academic performance. Our contribution is a methodology for developing annotations general enough to account for human life in open domains and to be consistent with both sensor data and sociological approaches.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917520
A. Gosain, I. Seskar
This demo presents the architecture of GENI (Global Environment of Network Innovations) [1] edge cloud computing network in the form of compute and storage systems, a mobile 4G LTE edge and a high speed campus network. GENI's edge computing strategy proceeds by deploying self-contained packages of network, computing, storage resources, or GENI Racks [2] connected via high speed fiber to LTE BS(s) across twelve campuses in the US, all interconnected via a nationwide research network. The GENI mobile computing resource manager is based on the Orbit Management framework (OMF) [3] and provides seamless access to the computing resources via the GENI Portal for experimentation, scheduling, data collection and processing of ubiquitous computing applications.
{"title":"GENI wireless testbed: An open edge ecosystem for ubiquitous computing applications","authors":"A. Gosain, I. Seskar","doi":"10.1109/PERCOMW.2017.7917520","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917520","url":null,"abstract":"This demo presents the architecture of GENI (Global Environment of Network Innovations) [1] edge cloud computing network in the form of compute and storage systems, a mobile 4G LTE edge and a high speed campus network. GENI's edge computing strategy proceeds by deploying self-contained packages of network, computing, storage resources, or GENI Racks [2] connected via high speed fiber to LTE BS(s) across twelve campuses in the US, all interconnected via a nationwide research network. The GENI mobile computing resource manager is based on the Orbit Management framework (OMF) [3] and provides seamless access to the computing resources via the GENI Portal for experimentation, scheduling, data collection and processing of ubiquitous computing applications.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917599
Guanqing Liang, Jiannong Cao, Xuefeng Liu
Poor sitting postures influence one's health and can cause upper limb and neck disorder. Current solutions for siting posture recognition, however, are impractical due to intrusiveness, high cost or low generalization capability. Particularly, most of the existing solutions are chair-dependent, which are highly coupled with certain types of chairs. In this paper, we design Postureware, a smart cushion, which is a low-cost, non-intrusive and general sitting posture recognition system. In particular, Postureware incorporates very thin pressure sensors to offer non-intrusive experience, an effective sensor placement solution to reduce cost, a set of user-invariant features and an ensemble learning classifier to improve generalization ability. We implement a prototype system and conduct extensive experiments. The results show that Postureware can classify fifteen fine-grained postures with high accuracy.
{"title":"Smart cushion: A practical system for fine-grained sitting posture recognition","authors":"Guanqing Liang, Jiannong Cao, Xuefeng Liu","doi":"10.1109/PERCOMW.2017.7917599","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917599","url":null,"abstract":"Poor sitting postures influence one's health and can cause upper limb and neck disorder. Current solutions for siting posture recognition, however, are impractical due to intrusiveness, high cost or low generalization capability. Particularly, most of the existing solutions are chair-dependent, which are highly coupled with certain types of chairs. In this paper, we design Postureware, a smart cushion, which is a low-cost, non-intrusive and general sitting posture recognition system. In particular, Postureware incorporates very thin pressure sensors to offer non-intrusive experience, an effective sensor placement solution to reduce cost, a set of user-invariant features and an ensemble learning classifier to improve generalization ability. We implement a prototype system and conduct extensive experiments. The results show that Postureware can classify fifteen fine-grained postures with high accuracy.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114496229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917573
Clement Lork, B. Rajasekhar, C. Yuen, N. Pindoriya
Due to the significant contribution of air-conditioning load towards total energy consumption in residential buildings, accurate modelling and forecasting of such load is key to effective demand-side energy management programmes. This paper suggests a data driven framework for 15 min-ahead AC load forecasting based on modern machine learning techniques that includes Support Vector Regression, Ensemble Trees, and Artificial Neural Network. To the end, it utilizes a correlation based feature selection method to identify information that is relevant for machine learning modelling. The effect of spatio-temporal features selection on prediction output and the effect of training data quantity on convergence characteristics were analysed and discussed. The effectiveness of the proposed approach is evaluated using a 20-household, half-year data set from an ongoing research testbed set up at the faculty housing units of Singapore University of Technology and Design. An linear combination method was proposed to combine models and the resulting model gave a mean absolute percentage error of 11.27%.
{"title":"How many watts: A data driven approach to aggregated residential air-conditioning load forecasting","authors":"Clement Lork, B. Rajasekhar, C. Yuen, N. Pindoriya","doi":"10.1109/PERCOMW.2017.7917573","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917573","url":null,"abstract":"Due to the significant contribution of air-conditioning load towards total energy consumption in residential buildings, accurate modelling and forecasting of such load is key to effective demand-side energy management programmes. This paper suggests a data driven framework for 15 min-ahead AC load forecasting based on modern machine learning techniques that includes Support Vector Regression, Ensemble Trees, and Artificial Neural Network. To the end, it utilizes a correlation based feature selection method to identify information that is relevant for machine learning modelling. The effect of spatio-temporal features selection on prediction output and the effect of training data quantity on convergence characteristics were analysed and discussed. The effectiveness of the proposed approach is evaluated using a 20-household, half-year data set from an ongoing research testbed set up at the faculty housing units of Singapore University of Technology and Design. An linear combination method was proposed to combine models and the resulting model gave a mean absolute percentage error of 11.27%.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126279080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917554
S. Taie, Wafa Ghonaim
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression. The framework consists of four steps: segmentation, feature extraction and feature reduction, classification, finally the parameter values of the classifier are dynamically optimized using the optimization algorithm Chicken Swarm Optimization (CSO) which is a bio-inspired optimization algorithm, and particle swarm optimization (PSO) optimizers to maximize the classification accuracy. We used 80, 100, 150 neuroimages training data set sizes to train the system and 100 out of sample neuroimages to test the system. The proposed system preliminary results demonstrate the efficacy and efficiency of the system to accurately detect and classify the brain tumor in MRI, that motivate us to expand applying of this system on other types of tumors in medical imagery.
{"title":"Title CSO-based algorithm with support vector machine for brain tumor's disease diagnosis","authors":"S. Taie, Wafa Ghonaim","doi":"10.1109/PERCOMW.2017.7917554","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917554","url":null,"abstract":"This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression. The framework consists of four steps: segmentation, feature extraction and feature reduction, classification, finally the parameter values of the classifier are dynamically optimized using the optimization algorithm Chicken Swarm Optimization (CSO) which is a bio-inspired optimization algorithm, and particle swarm optimization (PSO) optimizers to maximize the classification accuracy. We used 80, 100, 150 neuroimages training data set sizes to train the system and 100 out of sample neuroimages to test the system. The proposed system preliminary results demonstrate the efficacy and efficiency of the system to accurately detect and classify the brain tumor in MRI, that motivate us to expand applying of this system on other types of tumors in medical imagery.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917514
Xuhong Zhang, Venkata R. N. Mallepudi, C. Butts
The problem of collecting, processing, and learning from high-volume mobile device data has become an active research area in recent years. Time series data on application usage, in particular promises to provide fine-grained information on individual activity patterns, but currently poses collection and analysis challenges. In this paper we demonstrate an integrated system which can cheaply and easily collect application behavior and survey data from mobile phones; we introduce several novel features that assist the learning of individual level demographic features (e.g., gender and age group). Specifically, our approach for learning and inference for demographic features involves new techniques: (i) decomposing the app usage from mobile phones using spectral methods; (ii) learning spectral characteristics associated with individuals using a training set; (iii) combining other temporal features with learned spectral characteristics to predict demographic features for out-of-sample individuals. The core of our methodology is the utilization of spectral features in cell phone app activity series, allowing both identification of behavior patterns arising from particular types of cell phone apps and leveraging of those patterns for demographic classification and prediction. We demonstrate the effectiveness of our approach with an application to real mobile app traffic data from the United States.
{"title":"An integrated platform for collecting mobile phone data and learning demographic features","authors":"Xuhong Zhang, Venkata R. N. Mallepudi, C. Butts","doi":"10.1109/PERCOMW.2017.7917514","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917514","url":null,"abstract":"The problem of collecting, processing, and learning from high-volume mobile device data has become an active research area in recent years. Time series data on application usage, in particular promises to provide fine-grained information on individual activity patterns, but currently poses collection and analysis challenges. In this paper we demonstrate an integrated system which can cheaply and easily collect application behavior and survey data from mobile phones; we introduce several novel features that assist the learning of individual level demographic features (e.g., gender and age group). Specifically, our approach for learning and inference for demographic features involves new techniques: (i) decomposing the app usage from mobile phones using spectral methods; (ii) learning spectral characteristics associated with individuals using a training set; (iii) combining other temporal features with learned spectral characteristics to predict demographic features for out-of-sample individuals. The core of our methodology is the utilization of spectral features in cell phone app activity series, allowing both identification of behavior patterns arising from particular types of cell phone apps and leveraging of those patterns for demographic classification and prediction. We demonstrate the effectiveness of our approach with an application to real mobile app traffic data from the United States.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128939799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917507
Marek Bachmann, Michel Morold, K. David
Pedestrians globally comprise 22 % of all road traffic deaths in 2013. Various approaches for reducing accident numbers have already been introduced and are still being researched. Most of these approaches have specific limitations, like requiring line of sight. To overcome these limitations, we propose the Wireless Seat Belt (WSB), a smartphone-based collision avoidance system for pedestrians. Unlike other systems, the WSB uses context information, obtained from a pedestrian's smartphone, not only as additional information but also for using the information to improve the collision detection accuracy. The WSB introduces independent, individual modules for recognizing the pedestrian's direction, position, and speed. We first evaluate the influence of the measurement errors of each module on the missed alarm probability in a typical urban collision scenario using a simulator. Then, the impact of using the pedestrian's context to decrease the missed alarm probability is evaluated. The evaluation is done using the example of a curb detection module. The curb detection is used to infer that the pedestrian has stepped onto the street to correct the pedestrian's position. The results show a decrease of the missed alarm probability by 46.5 % in the scenario considered.
{"title":"Improving smartphone based collision avoidance by using pedestrian context information","authors":"Marek Bachmann, Michel Morold, K. David","doi":"10.1109/PERCOMW.2017.7917507","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917507","url":null,"abstract":"Pedestrians globally comprise 22 % of all road traffic deaths in 2013. Various approaches for reducing accident numbers have already been introduced and are still being researched. Most of these approaches have specific limitations, like requiring line of sight. To overcome these limitations, we propose the Wireless Seat Belt (WSB), a smartphone-based collision avoidance system for pedestrians. Unlike other systems, the WSB uses context information, obtained from a pedestrian's smartphone, not only as additional information but also for using the information to improve the collision detection accuracy. The WSB introduces independent, individual modules for recognizing the pedestrian's direction, position, and speed. We first evaluate the influence of the measurement errors of each module on the missed alarm probability in a typical urban collision scenario using a simulator. Then, the impact of using the pedestrian's context to decrease the missed alarm probability is evaluated. The evaluation is done using the example of a curb detection module. The curb detection is used to infer that the pedestrian has stepped onto the street to correct the pedestrian's position. The results show a decrease of the missed alarm probability by 46.5 % in the scenario considered.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128943587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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