Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).
{"title":"A model for WLAN signal attenuation of the human body","authors":"Ngewi Fet, M. Handte, P. Marrón","doi":"10.1145/2493432.2493459","DOIUrl":"https://doi.org/10.1145/2493432.2493459","url":null,"abstract":"Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123153269","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}
{"title":"Session details: Public displays","authors":"N. Davies","doi":"10.1145/3254806","DOIUrl":"https://doi.org/10.1145/3254806","url":null,"abstract":"","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448875","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}
{"title":"Session details: Hardware","authors":"Sidhant Gupta","doi":"10.1145/3254791","DOIUrl":"https://doi.org/10.1145/3254791","url":null,"abstract":"","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131532885","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}
Edison Thomaz, Aman Parnami, Jonathan Bidwell, Irfan Essa, G. Abowd
First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people's eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between the eating information in an image and its potential privacy concerns. Using data gathered by 5 participants wearing a lanyard-mounted smartphone, we show how the framework can be used to quantitatively assess the effectiveness of four automated techniques (face detection, image cropping, location filtering and motion filtering) at reducing the privacy-infringing content of images while still maintaining evidence of eating behaviors throughout the day.
{"title":"Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras","authors":"Edison Thomaz, Aman Parnami, Jonathan Bidwell, Irfan Essa, G. Abowd","doi":"10.1145/2493432.2493509","DOIUrl":"https://doi.org/10.1145/2493432.2493509","url":null,"abstract":"First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people's eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between the eating information in an image and its potential privacy concerns. Using data gathered by 5 participants wearing a lanyard-mounted smartphone, we show how the framework can be used to quantitatively assess the effectiveness of four automated techniques (face detection, image cropping, location filtering and motion filtering) at reducing the privacy-infringing content of images while still maintaining evidence of eating behaviors throughout the day.","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134484643","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}
{"title":"Session details: Crowdsourcing II","authors":"Nic Lane","doi":"10.1145/3254783","DOIUrl":"https://doi.org/10.1145/3254783","url":null,"abstract":"","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"10 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930851","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}
Yohan Chon, N. Lane, Yunjong Kim, Feng Zhao, H. Cha
Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
{"title":"Understanding the coverage and scalability of place-centric crowdsensing","authors":"Yohan Chon, N. Lane, Yunjong Kim, Feng Zhao, H. Cha","doi":"10.1145/2493432.2493498","DOIUrl":"https://doi.org/10.1145/2493432.2493498","url":null,"abstract":"Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121848142","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}
{"title":"Session details: Positioning I","authors":"A. LaMarca","doi":"10.1145/3254789","DOIUrl":"https://doi.org/10.1145/3254789","url":null,"abstract":"","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123724084","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}
{"title":"Session details: Activity recognition","authors":"A. Ferscha","doi":"10.1145/3254790","DOIUrl":"https://doi.org/10.1145/3254790","url":null,"abstract":"","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124287938","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}
Cuong Pham, D. Jackson, Johannes Schöning, Tom Bartindale, T. Plötz, P. Olivier
We describe FoodBoard, an instrumented chopping board that uses optical fibers and embedded camera imaging to identify unpackaged ingredients during food preparation on its surface. By embedding the sensing directly, and robustly, in the surface of a chopping board we also demonstrate how surface contact optical sensing can be used to realize the portability and privacy required of technology used in a setting such as a domestic kitchen. FoodBoard was subjected to a close to real-world evaluation in which 12 users prepared actual meals. FoodBoard compared favourably with existing unpackaged food recognition systems, classifying a larger number of distinct food ingredients (12 incl. meat, fruit, vegetables) with an average accuracy of 82.8%.
{"title":"FoodBoard: surface contact imaging for food recognition","authors":"Cuong Pham, D. Jackson, Johannes Schöning, Tom Bartindale, T. Plötz, P. Olivier","doi":"10.1145/2493432.2493522","DOIUrl":"https://doi.org/10.1145/2493432.2493522","url":null,"abstract":"We describe FoodBoard, an instrumented chopping board that uses optical fibers and embedded camera imaging to identify unpackaged ingredients during food preparation on its surface. By embedding the sensing directly, and robustly, in the surface of a chopping board we also demonstrate how surface contact optical sensing can be used to realize the portability and privacy required of technology used in a setting such as a domestic kitchen. FoodBoard was subjected to a close to real-world evaluation in which 12 users prepared actual meals. FoodBoard compared favourably with existing unpackaged food recognition systems, classifying a larger number of distinct food ingredients (12 incl. meat, fruit, vegetables) with an average accuracy of 82.8%.","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128347907","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}
Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).
{"title":"Automatically detecting problematic use of smartphones","authors":"Choonsung Shin, A. Dey","doi":"10.1145/2493432.2493443","DOIUrl":"https://doi.org/10.1145/2493432.2493443","url":null,"abstract":"Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129343982","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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