Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456512
Samuel Sungmin Cho, C. Julien
Acquiring local context information and sharing it among co-located devices is critical for emerging pervasive computing applications. The devices belonging to a group of co-located people may need to detect a shared activity (e.g., a meeting) to adapt their devices to support the activity. Today's devices are almost universally equipped with device-to-device communication that easily enables direct context sharing. While existing context sharing models tend not to consider devices' resource limitations or users' constraints, enabling devices to directly share context has significant benefits for efficiency, cost, and privacy. However, as we demonstrate quantitatively, when devices share context via device-to-device communication, it needs to be represented in a size-efficient way that does not sacrifice its expressiveness or accuracy. We present CHITCHAT, a suite of context representations that allows application developers to tune tradeoffs between the size of the representation, the flexibility of the application to update context information, the energy required to create and share context, and the quality of the information shared. We can substantially reduce the size of context representation (thereby reducing applications' overheads when they share their contexts with one another) with only a minimal reduction in the quality of shared contexts.
{"title":"Chitchat: Navigating tradeoffs in device-to-device context sharing","authors":"Samuel Sungmin Cho, C. Julien","doi":"10.1109/PERCOM.2016.7456512","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456512","url":null,"abstract":"Acquiring local context information and sharing it among co-located devices is critical for emerging pervasive computing applications. The devices belonging to a group of co-located people may need to detect a shared activity (e.g., a meeting) to adapt their devices to support the activity. Today's devices are almost universally equipped with device-to-device communication that easily enables direct context sharing. While existing context sharing models tend not to consider devices' resource limitations or users' constraints, enabling devices to directly share context has significant benefits for efficiency, cost, and privacy. However, as we demonstrate quantitatively, when devices share context via device-to-device communication, it needs to be represented in a size-efficient way that does not sacrifice its expressiveness or accuracy. We present CHITCHAT, a suite of context representations that allows application developers to tune tradeoffs between the size of the representation, the flexibility of the application to update context information, the energy required to create and share context, and the quality of the information shared. We can substantially reduce the size of context representation (thereby reducing applications' overheads when they share their contexts with one another) with only a minimal reduction in the quality of shared contexts.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124471222","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456514
Sugang Li, A. Ashok, Yanyong Zhang, Chenren Xu, J. Lindqvist, M. Gruteser
In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.
{"title":"Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns","authors":"Sugang Li, A. Ashok, Yanyong Zhang, Chenren Xu, J. Lindqvist, M. Gruteser","doi":"10.1109/PERCOM.2016.7456514","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456514","url":null,"abstract":"In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130612932","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456508
Jörg Müller, T. Langlotz, H. Regenbrecht
We are presenting PanoVC - a mobile telepresence system based on continuously updated panoramic images. We are showing that the experience of telepresence, i.e. the sense of "being there together" at a distant location can be achieved with standard state-of-the-art mobile phones. Because mobile phones are always on hand users can share their environments with others in a pervasive way. Our approach is opening up the pathway for applications in a variety of domains such as the exploration of remote environments or novel forms of videoconferencing. We present implementation details, technical evaluation results, and the findings of a user study of an indoor-outdoor environments sharing task as proof of concept.
{"title":"PanoVC: Pervasive telepresence using mobile phones","authors":"Jörg Müller, T. Langlotz, H. Regenbrecht","doi":"10.1109/PERCOM.2016.7456508","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456508","url":null,"abstract":"We are presenting PanoVC - a mobile telepresence system based on continuously updated panoramic images. We are showing that the experience of telepresence, i.e. the sense of \"being there together\" at a distant location can be achieved with standard state-of-the-art mobile phones. Because mobile phones are always on hand users can share their environments with others in a pervasive way. Our approach is opening up the pathway for applications in a variety of domains such as the exploration of remote environments or novel forms of videoconferencing. We present implementation details, technical evaluation results, and the findings of a user study of an indoor-outdoor environments sharing task as proof of concept.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114912566","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456511
Lorenz Schwittmann, V. Matkovic, Matthäus Wander, Torben Weis
We present a method for recognizing a video that is playing on a TV screen by sampling the ambient light sensor of a user's smartphone. This improves situation awareness in pervasive systems because the phone can determine what the user is currently watching on TV. Our method works even if the phone has no direct line of sight to the TV screen, since ambient light reflected from walls is sufficient. Our evaluation shows that a 100% recognition ratio of the current TV channel is possible by sampling a sequence of 15 to 120 seconds length, depending on more or less favorable measuring conditions. In addition, we evaluated the recognition ratio when the user is watching video-on-demand, which exhibits a large set of possible videos. Recognizing professional YouTube videos resulted in a 92% recognition ratio; amateur videos were recognized correctly with 60% because these videos have fewer cuts. Our method focuses on detecting the time difference between video cuts because the light emitted by the screen changes instantly with most cuts and this is easily measurable with the ambient light sensor. Using the ambient light sensor instead of the camera greatly benefits energy consumption, bandwidth usage and raises less privacy concerns. Hence, it is feasible to run the measurement in the background for a longer time without draining the battery and without sending camera shots to a remote server for analysis.
{"title":"Video recognition using ambient light sensors","authors":"Lorenz Schwittmann, V. Matkovic, Matthäus Wander, Torben Weis","doi":"10.1109/PERCOM.2016.7456511","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456511","url":null,"abstract":"We present a method for recognizing a video that is playing on a TV screen by sampling the ambient light sensor of a user's smartphone. This improves situation awareness in pervasive systems because the phone can determine what the user is currently watching on TV. Our method works even if the phone has no direct line of sight to the TV screen, since ambient light reflected from walls is sufficient. Our evaluation shows that a 100% recognition ratio of the current TV channel is possible by sampling a sequence of 15 to 120 seconds length, depending on more or less favorable measuring conditions. In addition, we evaluated the recognition ratio when the user is watching video-on-demand, which exhibits a large set of possible videos. Recognizing professional YouTube videos resulted in a 92% recognition ratio; amateur videos were recognized correctly with 60% because these videos have fewer cuts. Our method focuses on detecting the time difference between video cuts because the light emitted by the screen changes instantly with most cuts and this is easily measurable with the ambient light sensor. Using the ambient light sensor instead of the camera greatly benefits energy consumption, bandwidth usage and raises less privacy concerns. Hence, it is feasible to run the measurement in the background for a longer time without draining the battery and without sending camera shots to a remote server for analysis.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668801","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456502
Jiahui Wen, J. Indulska, Mingyang Zhong
Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.
{"title":"Adaptive activity learning with dynamically available context","authors":"Jiahui Wen, J. Indulska, Mingyang Zhong","doi":"10.1109/PERCOM.2016.7456502","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456502","url":null,"abstract":"Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487364","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456522
F. Zambonelli, W. Meuter, S. Kanhere, S. Loke, Flora D. Salim
Pervasive and mobile computing technologies can make our everyday living environments and our cities "smart", i.e., capable of reaching awareness of physical and social processes and of dynamically affecting them in a purposeful way. In general, living in a smart environment and being made part of its activities somehow make us - as individuals - smarter as well, by increasing our perceptory and social capabilities. However, a potential risk could be to start delegating too much to the environment itself, losing in critical attention, abandoning individual decision making for relying on collective computational governance of our activity, and in the end also losing awareness of environmental and social processes. The panel intends to discuss the above issues with the help of relevant researchers in the area of pervasive computing, smart environments, collective intelligence.
{"title":"Smart cities: Intelligent environments and dumb people? Panel summary","authors":"F. Zambonelli, W. Meuter, S. Kanhere, S. Loke, Flora D. Salim","doi":"10.1109/PERCOM.2016.7456522","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456522","url":null,"abstract":"Pervasive and mobile computing technologies can make our everyday living environments and our cities \"smart\", i.e., capable of reaching awareness of physical and social processes and of dynamically affecting them in a purposeful way. In general, living in a smart environment and being made part of its activities somehow make us - as individuals - smarter as well, by increasing our perceptory and social capabilities. However, a potential risk could be to start delegating too much to the environment itself, losing in critical attention, abandoning individual decision making for relying on collective computational governance of our activity, and in the end also losing awareness of environmental and social processes. The panel intends to discuss the above issues with the help of relevant researchers in the area of pervasive computing, smart environments, collective intelligence.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115753024","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456504
Allan Stisen, Andreas Mathisen, S. Sørensen, H. Blunck, M. Kjærgaard, Thor S. Prentow
Being aware of activities of co-workers is a basic and vital mechanism for efficient work in highly distributed work settings. Thus, automatic recognition of the task phases the mobile workers are currently (or have been) in has many applications, e.g., efficient coordination of tasks by visualizing co-workers' task progress, automatic notifications based on context awareness, and record filing of task statuses and completions. This paper presents methods to sense and detect highly mobile workers' tasks phases in large building complexes. Large building complexes restrict the technologies available for sensing and recognizing the activities and task phases the workers currently perform as such technologies have to be easily deployable and maintainable at a large scale. The methods presented in this paper consist of features that utilize data from sensing systems which are common in large-scale indoor work environments, namely from a WiFi infrastructure providing coarse grained indoor positioning, from inertial sensors in the workers' mobile phones, and from a task management system yielding information about the scheduled tasks' start and end locations. The methods presented have low requirements on the accuracy of the indoor positioning, and thus come with low deployment and maintenance effort in real-world settings. We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on manually labelled real-world data collected over 4 days of regular work life of the mobile workforce. The collected data yields 83 tasks in total involving 8 different orderlies from a major university hospital with a building area of 160, 000 m2. The results show that the proposed methods can distinguish accurately between the four most common task phases present in the orderlies' work routines, achieving Fi-Scores of 89.2%.
{"title":"Task phase recognition for highly mobile workers in large building complexes","authors":"Allan Stisen, Andreas Mathisen, S. Sørensen, H. Blunck, M. Kjærgaard, Thor S. Prentow","doi":"10.1109/PERCOM.2016.7456504","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456504","url":null,"abstract":"Being aware of activities of co-workers is a basic and vital mechanism for efficient work in highly distributed work settings. Thus, automatic recognition of the task phases the mobile workers are currently (or have been) in has many applications, e.g., efficient coordination of tasks by visualizing co-workers' task progress, automatic notifications based on context awareness, and record filing of task statuses and completions. This paper presents methods to sense and detect highly mobile workers' tasks phases in large building complexes. Large building complexes restrict the technologies available for sensing and recognizing the activities and task phases the workers currently perform as such technologies have to be easily deployable and maintainable at a large scale. The methods presented in this paper consist of features that utilize data from sensing systems which are common in large-scale indoor work environments, namely from a WiFi infrastructure providing coarse grained indoor positioning, from inertial sensors in the workers' mobile phones, and from a task management system yielding information about the scheduled tasks' start and end locations. The methods presented have low requirements on the accuracy of the indoor positioning, and thus come with low deployment and maintenance effort in real-world settings. We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on manually labelled real-world data collected over 4 days of regular work life of the mobile workforce. The collected data yields 83 tasks in total involving 8 different orderlies from a major university hospital with a building area of 160, 000 m2. The results show that the proposed methods can distinguish accurately between the four most common task phases present in the orderlies' work routines, achieving Fi-Scores of 89.2%.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121175768","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456510
J. Begole
Summary form only given. What will be the next computing paradigm as the ubiquitous computing wave crests: autonomobiles, robots, virtual reality, internet of things, intelligent agents? While pundits search for the next big thing among a dizzying array of shiny ideas, the truth is that pervasive technologies have reached critical mass that we no longer need to ask "what if" and we can shift our attention to "what when". One picture of that future is a recasting of how we think about designing digital experiences - rather than systems that react to user direction, we can design systems that respond dynamically to the users' attention, engagement and context: Responsive Media. The same machine learning technologies that have made speech and image recognition surprisingly accurate are also enhancing our devices' abilities to sense user activities, emotions and intentions and to deliver services and information proactively. Media experiences will be dramatically changed by the next generation of these technologies embedded into smartphones and VR goggles and robots and smart homes and autonomobiles so that they not only sense the audience's engagement in real time, but they can also predict disengagement and prevent it by dynamically shifting the content to appeal to an individual's preferences, emotion state and situation. More than just media experiences, imagine robots that can sense a child's frustration and actively assist in the homework, digital assistants that do not interrupt inappropriately, semi-autonomobiles that ensure the media is not disrupting the driver's attention demands, and more. Responsive media will be more like an engaging conversation among humans, rather than just passive consumption. What are the requirements for a conversational interaction? This talk will paint a picture and challenge the audience to identify the remaining technology barriers, architectures, business ecosystems, threats, and yes, the killer applications. I seek your input as we create the future beyond ubiquitous computing.
{"title":"The dawn of the age of responsive media (Keynote abstract)","authors":"J. Begole","doi":"10.1109/PERCOM.2016.7456510","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456510","url":null,"abstract":"Summary form only given. What will be the next computing paradigm as the ubiquitous computing wave crests: autonomobiles, robots, virtual reality, internet of things, intelligent agents? While pundits search for the next big thing among a dizzying array of shiny ideas, the truth is that pervasive technologies have reached critical mass that we no longer need to ask \"what if\" and we can shift our attention to \"what when\". One picture of that future is a recasting of how we think about designing digital experiences - rather than systems that react to user direction, we can design systems that respond dynamically to the users' attention, engagement and context: Responsive Media. The same machine learning technologies that have made speech and image recognition surprisingly accurate are also enhancing our devices' abilities to sense user activities, emotions and intentions and to deliver services and information proactively. Media experiences will be dramatically changed by the next generation of these technologies embedded into smartphones and VR goggles and robots and smart homes and autonomobiles so that they not only sense the audience's engagement in real time, but they can also predict disengagement and prevent it by dynamically shifting the content to appeal to an individual's preferences, emotion state and situation. More than just media experiences, imagine robots that can sense a child's frustration and actively assist in the homework, digital assistants that do not interrupt inappropriately, semi-autonomobiles that ensure the media is not disrupting the driver's attention demands, and more. Responsive media will be more like an engaging conversation among humans, rather than just passive consumption. What are the requirements for a conversational interaction? This talk will paint a picture and challenge the audience to identify the remaining technology barriers, architectures, business ecosystems, threats, and yes, the killer applications. I seek your input as we create the future beyond ubiquitous computing.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129707320","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456513
Claudio Martella, Armando Miraglia, M. Cattani, M. Steen
Face-to-face proximity has been successfully leveraged to study the relationships between individuals in various contexts, from a working place, to a conference, a museum, a fair, and a date. We spend time facing the individuals with whom we chat, discuss, work, and play. However, face-to-face proximity is not the realm of solely person-to-person relationships, but it can be used as a proxy to study person-to-object relationships as well. We face the objects with which we interact on a daily basis, like a television, the kitchen appliances, a book, including more complex objects like a stage where a concert is taking place. In this paper, we focus on the relationship between the visitors of an art exhibition and its exhibits. We design, implement, and deploy a sensing infrastructure based on inexpensive mobile proximity sensors and a filtering pipeline that we use to measure face-to-face proximity between individuals and exhibits. Our pipeline produces an improvement in measurement accuracy of up to 64% relative to raw data. We use this data to mine the behavior of the visitors and show that group behavior can be recognized by means of data clustering and visualization.
{"title":"Leveraging proximity sensing to mine the behavior of museum visitors","authors":"Claudio Martella, Armando Miraglia, M. Cattani, M. Steen","doi":"10.1109/PERCOM.2016.7456513","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456513","url":null,"abstract":"Face-to-face proximity has been successfully leveraged to study the relationships between individuals in various contexts, from a working place, to a conference, a museum, a fair, and a date. We spend time facing the individuals with whom we chat, discuss, work, and play. However, face-to-face proximity is not the realm of solely person-to-person relationships, but it can be used as a proxy to study person-to-object relationships as well. We face the objects with which we interact on a daily basis, like a television, the kitchen appliances, a book, including more complex objects like a stage where a concert is taking place. In this paper, we focus on the relationship between the visitors of an art exhibition and its exhibits. We design, implement, and deploy a sensing infrastructure based on inexpensive mobile proximity sensors and a filtering pipeline that we use to measure face-to-face proximity between individuals and exhibits. Our pipeline produces an improvement in measurement accuracy of up to 64% relative to raw data. We use this data to mine the behavior of the visitors and show that group behavior can be recognized by means of data clustering and visualization.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"474 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114844237","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 : 2016-03-14DOI: 10.1109/PERCOM.2016.7456525
Xuhong Zhang, C. Butts
In this paper we demonstrate a novel approach to the use of spatio-temporally aggregated cell phone data to learn features of urban ecology (i.e., spatial distributions of distinct social and economic entities and their associated activities). Specifically, our technique involves four stages: (i) decomposing the aggregated cell phone activity within local areal units using spectral methods; (ii) learning spectral characteristics associated with ecological features using a training set; (iii) predicting local ecology composition for out-of-sample areas; and (iv) predicting activity time series for out-of-sample areas. The core of our approach is the projection of spectral features in cell phone activity series into an ecology-associated basis, allowing both identification of communication patterns arising from particular types of local activities and/or institutions and leveraging of those patterns for classification and activity prediction. We apply our methodology to aggregated communication and Internet traffic data from the cities of Milan and Trento to show the effectiveness of our method.
{"title":"A novel multivariate spectral regression model for learning relationships between communication activity and urban ecology","authors":"Xuhong Zhang, C. Butts","doi":"10.1109/PERCOM.2016.7456525","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456525","url":null,"abstract":"In this paper we demonstrate a novel approach to the use of spatio-temporally aggregated cell phone data to learn features of urban ecology (i.e., spatial distributions of distinct social and economic entities and their associated activities). Specifically, our technique involves four stages: (i) decomposing the aggregated cell phone activity within local areal units using spectral methods; (ii) learning spectral characteristics associated with ecological features using a training set; (iii) predicting local ecology composition for out-of-sample areas; and (iv) predicting activity time series for out-of-sample areas. The core of our approach is the projection of spectral features in cell phone activity series into an ecology-associated basis, allowing both identification of communication patterns arising from particular types of local activities and/or institutions and leveraging of those patterns for classification and activity prediction. We apply our methodology to aggregated communication and Internet traffic data from the cities of Milan and Trento to show the effectiveness of our method.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581578","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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