Pub Date : 2013-03-18DOI: 10.1109/PerComW.2013.6529493
H. Kanasugi, Y. Sekimoto, Mori Kurokawa, Takafumi Watanabe, S. Muramatsu, R. Shibasaki
Continuous personal position information has been attracting attention in a variety of service and research areas. In recent years, many studies have applied the telecommunication histories of mobile phones (CDRs: call detail records) to position acquisition. Although large-scale and long-term data are accumulated from CDRs through everyday use of mobile phones, the spatial resolution of CDRs is lower than that of existing positioning technologies. Therefore, interpolating spatiotemporal positions of such sparse CDRs in accordance with human behavior models will facilitate services and researches. In this paper, we propose a new method to compensate for CDR drawbacks in tracking positions. We generate as many candidate routes as possible in the spatiotemporal domain using trip patterns interpolated using road and railway networks and select the most likely route from them. Trip patterns are feasible combinations between stay places that are detected from individual location histories in CDRs. The most likely route could be estimated through comparing candidate routes to observed CDRs during a target day. We also show the assessment of our method using CDRs and GPS logs obtained in the experimental survey.
{"title":"Spatiotemporal route estimation consistent with human mobility using cellular network data","authors":"H. Kanasugi, Y. Sekimoto, Mori Kurokawa, Takafumi Watanabe, S. Muramatsu, R. Shibasaki","doi":"10.1109/PerComW.2013.6529493","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529493","url":null,"abstract":"Continuous personal position information has been attracting attention in a variety of service and research areas. In recent years, many studies have applied the telecommunication histories of mobile phones (CDRs: call detail records) to position acquisition. Although large-scale and long-term data are accumulated from CDRs through everyday use of mobile phones, the spatial resolution of CDRs is lower than that of existing positioning technologies. Therefore, interpolating spatiotemporal positions of such sparse CDRs in accordance with human behavior models will facilitate services and researches. In this paper, we propose a new method to compensate for CDR drawbacks in tracking positions. We generate as many candidate routes as possible in the spatiotemporal domain using trip patterns interpolated using road and railway networks and select the most likely route from them. Trip patterns are feasible combinations between stay places that are detected from individual location histories in CDRs. The most likely route could be estimated through comparing candidate routes to observed CDRs during a target day. We also show the assessment of our method using CDRs and GPS logs obtained in the experimental survey.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143946","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529540
E. Gelenbe
The Quality of Information (QoI) can be evaluated through the effect that the information will have on a system which is of critical interest. Although QoI is often be discussed in the context of sensor networks, this paper addresses QoI in a new and important framework: the management of energy distribution. We consider a system that combines constant power generation by some conventional source, together with renewable energy being generated and stored. The consumer has some fixed contract with the conventional energy source and obtains any excess needed energy from storage. We show that imperfections in the interpretation or delivery of information about the consumer's instantaneous needs can lead to measurable deficiencies in energy provisioning. The results are derived using Energy Packet Networks which are a novel approach to modeling energy systems based on queueing theory.
{"title":"Quality of information and energy provisioning (invited paper)","authors":"E. Gelenbe","doi":"10.1109/PerComW.2013.6529540","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529540","url":null,"abstract":"The Quality of Information (QoI) can be evaluated through the effect that the information will have on a system which is of critical interest. Although QoI is often be discussed in the context of sensor networks, this paper addresses QoI in a new and important framework: the management of energy distribution. We consider a system that combines constant power generation by some conventional source, together with renewable energy being generated and stored. The consumer has some fixed contract with the conventional energy source and obtains any excess needed energy from storage. We show that imperfections in the interpretation or delivery of information about the consumer's instantaneous needs can lead to measurable deficiencies in energy provisioning. The results are derived using Energy Packet Networks which are a novel approach to modeling energy systems based on queueing theory.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"4 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125289047","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529446
Daqing Zhang
Since the seminal work of Schilit and Theimer on context-awareness in 1994, great research progress has been made in context-aware computing field. Due to limited deployment scale of sensors and devices, in early years context-aware computing focused mainly on understanding and exploiting personal context in single smart spaces. As a result of the recent explosion of sensor-equipped mobile phones, the phenomenal growth of Internet and social network services, the broader use of the Global Positioning System (GPS) in all types of public transportation, and the extensive deployment of sensor network and WiFi in both indoor and outdoor environments, the digital footprints left by people while interacting with cyber-physical spaces are accumulating with an unprecedented speed and scale. The technology trend towards crowd sensing is creating new challenges and opportunities for context-aware computing - with huge amount, large scale, multi-modal, different granularity, diverse quality of data from various data sources. In this talk, I will present a new research direction called “social and community intelligence (SCI)” as a natural extension of context-aware computing in the era of crowd sensing, with emphasis on extracting community and society level context; in particular I will introduce our work in mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities. Finally I will briefly summarize the difference between traditional context-aware computing and SCI in terms of data acquisition, modeling, inference, storage and context inferred.
{"title":"Keynote: Context-aware computing in the era of crowd sensing from personal and space context to social and community context","authors":"Daqing Zhang","doi":"10.1109/PerComW.2013.6529446","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529446","url":null,"abstract":"Since the seminal work of Schilit and Theimer on context-awareness in 1994, great research progress has been made in context-aware computing field. Due to limited deployment scale of sensors and devices, in early years context-aware computing focused mainly on understanding and exploiting personal context in single smart spaces. As a result of the recent explosion of sensor-equipped mobile phones, the phenomenal growth of Internet and social network services, the broader use of the Global Positioning System (GPS) in all types of public transportation, and the extensive deployment of sensor network and WiFi in both indoor and outdoor environments, the digital footprints left by people while interacting with cyber-physical spaces are accumulating with an unprecedented speed and scale. The technology trend towards crowd sensing is creating new challenges and opportunities for context-aware computing - with huge amount, large scale, multi-modal, different granularity, diverse quality of data from various data sources. In this talk, I will present a new research direction called “social and community intelligence (SCI)” as a natural extension of context-aware computing in the era of crowd sensing, with emphasis on extracting community and society level context; in particular I will introduce our work in mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities. Finally I will briefly summarize the difference between traditional context-aware computing and SCI in terms of data acquisition, modeling, inference, storage and context inferred.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129817092","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529573
P. Cottone, G. Re, Gabriele Maida, M. Morana
In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is based on the estimation of some relevant joints of the human body by using Kinect depth information. The most significative configurations of joints positions are combined by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, Hidden Markov Models (HMMs) are applied to model each activity as a sequence of known postures. The proposed solution has been tested on a public dataset while considering four different configurations corresponding to some state-of-the-art approaches and results are very promising. Moreover, in order to maintain a high level of pervasiveness, we implemented a real prototype by connecting Kinect sensor to a miniature computer capable of real-time processing.
{"title":"Motion sensors for activity recognition in an ambient-intelligence scenario","authors":"P. Cottone, G. Re, Gabriele Maida, M. Morana","doi":"10.1109/PerComW.2013.6529573","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529573","url":null,"abstract":"In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is based on the estimation of some relevant joints of the human body by using Kinect depth information. The most significative configurations of joints positions are combined by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, Hidden Markov Models (HMMs) are applied to model each activity as a sequence of known postures. The proposed solution has been tested on a public dataset while considering four different configurations corresponding to some state-of-the-art approaches and results are very promising. Moreover, in order to maintain a high level of pervasiveness, we implemented a real prototype by connecting Kinect sensor to a miniature computer capable of real-time processing.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129981316","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529469
K. Frank, P. Robertson, Michael Gross, Kevin Wiesner
In this work we present a mobile stress recognition system based on an existing activity recognition system using a hip-worn inertial measurement unit and a chest belt. Integrating activity knowledge, the prediction of different human stress levels in a mobile environment can be enabled while the state of the art is focussed on stress recognition in static environments. Our system has been implemented on an Android mobile phone and evaluated for different Bayesian networks as classifiers. Our implementation is able to operate in real-time with a stress inference rate of 1 Hz. The results of this work indicate that the implemented system is able to differentiate between the states 'No Stress' and 'Stress' in a mobile context. A more detailed distinction of stress in five substates has not been possible in a reliable way to date. With our results, the proposed system can serve as a basis for further improvements with larger data sets and for in-situ testing during disaster assessment.
{"title":"Sensor-based identification of human stress levels","authors":"K. Frank, P. Robertson, Michael Gross, Kevin Wiesner","doi":"10.1109/PerComW.2013.6529469","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529469","url":null,"abstract":"In this work we present a mobile stress recognition system based on an existing activity recognition system using a hip-worn inertial measurement unit and a chest belt. Integrating activity knowledge, the prediction of different human stress levels in a mobile environment can be enabled while the state of the art is focussed on stress recognition in static environments. Our system has been implemented on an Android mobile phone and evaluated for different Bayesian networks as classifiers. Our implementation is able to operate in real-time with a stress inference rate of 1 Hz. The results of this work indicate that the implemented system is able to differentiate between the states 'No Stress' and 'Stress' in a mobile context. A more detailed distinction of stress in five substates has not been possible in a reliable way to date. With our results, the proposed system can serve as a basis for further improvements with larger data sets and for in-situ testing during disaster assessment.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587313","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529579
Haesung Lee, Joonhee Kwon
Recently, many mobile techniques such as sensor networks or various types of mobile devices make it possible to provide smart services at any time, and anywhere. In despite of these remarkable advances of techniques, there are few personalized mobile recommendation services which fully consider user's current situation. Proposed recommendation algorithm efficiently defines user's current situation with situational data captured from various smartphone sensors. Also, the algorithm uses user's social network for efficiently filtering valuable items which are considered as authorities. To verify the usefulness of proposed technique, we implement a prototype of the personalized music recommendation service in which proposed recommendation technique is applied. Additionally, through the demonstration of implemented prototype, we investigate the effect of incorporating smartphone sensor data and social data to collaborative filtering algorithms.
{"title":"Situation and social awareness-based personalized recommendation service in pervasive computing environment","authors":"Haesung Lee, Joonhee Kwon","doi":"10.1109/PerComW.2013.6529579","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529579","url":null,"abstract":"Recently, many mobile techniques such as sensor networks or various types of mobile devices make it possible to provide smart services at any time, and anywhere. In despite of these remarkable advances of techniques, there are few personalized mobile recommendation services which fully consider user's current situation. Proposed recommendation algorithm efficiently defines user's current situation with situational data captured from various smartphone sensors. Also, the algorithm uses user's social network for efficiently filtering valuable items which are considered as authorities. To verify the usefulness of proposed technique, we implement a prototype of the personalized music recommendation service in which proposed recommendation technique is applied. Additionally, through the demonstration of implemented prototype, we investigate the effect of incorporating smartphone sensor data and social data to collaborative filtering algorithms.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549434","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529518
Iacopo Carreras, D. Miorandi, A. Tamilin, E. Ssebaggala, N. Conci
Crowd-sensing is becoming a popular computing and sensing paradigm for enclosing humans in the sensing loop. The underlying idea is that people, together with their mobile device, can act as mobile and pervasive sensors, gathering information about the surrounding environment and potentially providing direct input. In this work we focus on how to embed context-awareness in a crowd-sensing system in order preserve the battery of user's mobile device, while maximizing the user participation to crowd-sensing campaigns. We present the design and implementation of the Matador platform, and a preliminary evaluation obtained through a small-scale pilot study.
{"title":"Crowd-sensing: Why context matters","authors":"Iacopo Carreras, D. Miorandi, A. Tamilin, E. Ssebaggala, N. Conci","doi":"10.1109/PerComW.2013.6529518","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529518","url":null,"abstract":"Crowd-sensing is becoming a popular computing and sensing paradigm for enclosing humans in the sensing loop. The underlying idea is that people, together with their mobile device, can act as mobile and pervasive sensors, gathering information about the surrounding environment and potentially providing direct input. In this work we focus on how to embed context-awareness in a crowd-sensing system in order preserve the battery of user's mobile device, while maximizing the user participation to crowd-sensing campaigns. We present the design and implementation of the Matador platform, and a preliminary evaluation obtained through a small-scale pilot study.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115983679","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529528
G. Bahle, P. Lukowicz, K. Kunze, K. Kise
In this paper we investigate how vision based devices (cameras or the Kinect controller) that happen to be in the users' environment can be used to improve and fine tune on body sensor systems for activity recognition. Thus we imagine a user with his on body activity recognition system passing through a space with a video camera (or a Kinect), picking up some information, and using it to improve his system. The general idea is to correlate an anonymous ”stick figure” like description of the motion of a user's body parts provided by the vision system with the sensor signals as a means of analyzing the sensors' properties. In the paper we for example demonstrate how such a correlation can be used to determine, without the need to train any classifiers, on which body part a motion sensor is worn.
{"title":"I see you: How to improve wearable activity recognition by leveraging information from environmental cameras","authors":"G. Bahle, P. Lukowicz, K. Kunze, K. Kise","doi":"10.1109/PerComW.2013.6529528","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529528","url":null,"abstract":"In this paper we investigate how vision based devices (cameras or the Kinect controller) that happen to be in the users' environment can be used to improve and fine tune on body sensor systems for activity recognition. Thus we imagine a user with his on body activity recognition system passing through a space with a video camera (or a Kinect), picking up some information, and using it to improve his system. The general idea is to correlate an anonymous ”stick figure” like description of the motion of a user's body parts provided by the vision system with the sensor signals as a means of analyzing the sensors' properties. In the paper we for example demonstrate how such a correlation can be used to determine, without the need to train any classifiers, on which body part a motion sensor is worn.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127090751","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529523
Kuldeep Yadav, D. Chakraborty, Sonia Soubam, Naveen Prathapaneni, Vikrant Nandakumar, Vinayak Naik, N. Rajamani, L. V. Subramaniam, S. Mehta, Pradipta De
With the growing number of cities and population, continuous monitoring of city's infrastructure and automated collection of day-to-day events (such as traffic jam) is essential and can help in improving life style of citizens. It is extremely costly and ineffective to install hardware sensors to sense these events in developing regions. Due to advent of smartphones, citizens can play role of sensors and actively participate in collection of the events which can be shared with others for information or can be used in decisions which affects city development. In this paper, we describe an architecture of crowdsensing testbed for capturing and processing events affecting citizens in cities in India. One of the design principle of our testbed is that it encourages users to do an open-ended sensing under five broad categories: Civic complaints, traffic, neighbourhood issues, emergency and others. As part of testbed, we allow events submissions from different submission modes i.e. mobile application, SMSes and web. Our mobile application exploits different sensing interfaces provided by today's smartphones to add contextual data with event reports such as images, audio, fine-grained location etc. Proposed testbed is used by university students across India to report event happening around them. Finally, we describe the data collected and uncover some of challenges and opportunities which may help future designs of crowdsensing based systems.
{"title":"Human sensors: Case-study of open-ended community sensing in developing regions","authors":"Kuldeep Yadav, D. Chakraborty, Sonia Soubam, Naveen Prathapaneni, Vikrant Nandakumar, Vinayak Naik, N. Rajamani, L. V. Subramaniam, S. Mehta, Pradipta De","doi":"10.1109/PerComW.2013.6529523","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529523","url":null,"abstract":"With the growing number of cities and population, continuous monitoring of city's infrastructure and automated collection of day-to-day events (such as traffic jam) is essential and can help in improving life style of citizens. It is extremely costly and ineffective to install hardware sensors to sense these events in developing regions. Due to advent of smartphones, citizens can play role of sensors and actively participate in collection of the events which can be shared with others for information or can be used in decisions which affects city development. In this paper, we describe an architecture of crowdsensing testbed for capturing and processing events affecting citizens in cities in India. One of the design principle of our testbed is that it encourages users to do an open-ended sensing under five broad categories: Civic complaints, traffic, neighbourhood issues, emergency and others. As part of testbed, we allow events submissions from different submission modes i.e. mobile application, SMSes and web. Our mobile application exploits different sensing interfaces provided by today's smartphones to add contextual data with event reports such as images, audio, fine-grained location etc. Proposed testbed is used by university students across India to report event happening around them. Finally, we describe the data collected and uncover some of challenges and opportunities which may help future designs of crowdsensing based systems.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487980","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 : 2013-03-18DOI: 10.1109/PerComW.2013.6529509
Bin Guo, Zhiwen Yu, Xingshe Zhou, Daqing Zhang
Human memory is important yet often not easy to be handled in daily life. Many challenges are raised, such as how to enhance memory recall and reminiscence, how to facilitate memory sharing in terms of people's social nature. This paper proposes MemPhone, a new system that addresses various human memory needs by using the mobile tagging (e.g., RFID, barcodes) technique. By linking human memory or experience with associated physical objects, MemPhone can i) augment memory externalization and recall, and ii) build object-based social networks (OBSNs) to enhance memory sharing. By embedding physical contexts into SNs, the OBSN can strengthen friendships by enabling serendipity discovering and nurture new connections among people with shared memories. Early studies indicate that our system can facilitate memory recall and shared memory discovery.
{"title":"MemPhone: From personal memory aid to community memory sharing using mobile tagging","authors":"Bin Guo, Zhiwen Yu, Xingshe Zhou, Daqing Zhang","doi":"10.1109/PerComW.2013.6529509","DOIUrl":"https://doi.org/10.1109/PerComW.2013.6529509","url":null,"abstract":"Human memory is important yet often not easy to be handled in daily life. Many challenges are raised, such as how to enhance memory recall and reminiscence, how to facilitate memory sharing in terms of people's social nature. This paper proposes MemPhone, a new system that addresses various human memory needs by using the mobile tagging (e.g., RFID, barcodes) technique. By linking human memory or experience with associated physical objects, MemPhone can i) augment memory externalization and recall, and ii) build object-based social networks (OBSNs) to enhance memory sharing. By embedding physical contexts into SNs, the OBSN can strengthen friendships by enabling serendipity discovering and nurture new connections among people with shared memories. Early studies indicate that our system can facilitate memory recall and shared memory discovery.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130810914","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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