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Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on

Date 24-28 March 2014

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Displaying Results 1 - 25 of 32
  • [Copyright notice]

    Publication Year: 2014 , Page(s): 1
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  • General chairs welcome welcome message from the general chairs

    Publication Year: 2014 , Page(s): 1
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  • TPC welcome welcome message from the technical program chairs

    Publication Year: 2014 , Page(s): 1
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  • Committees

    Publication Year: 2014 , Page(s): 1 - 3
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  • Technical program

    Publication Year: 2014 , Page(s): 1 - 4
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  • Keynote: WInternet: From Net of Things to Internet of Things

    Publication Year: 2014 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (223 KB) |  | HTML iconHTML  

    Summary form only given. Internet of Things (IoT) is a networking infrastructure for cyber-physical systems. With IoT, physical objects should be seamlessly integrated into an Internet-like system so that the physical objects and cyber-agents can interact each other in order to achieve mission-critical objectives. Given its tremendous application potential, IoT has become popular in recent years, attracting great attentions from both academic research and industrial development. In this talk, we will first focus on fundamental issues related to IoT. We address principles that should guide research and development of IoT. We will then present several approaches that may lead to implementation of IoT and analyze their advantages and disadvantages. We will show an implementation of IoT called “WInternet” and demonstrate its application. Finally, we will discuss critical issues that must be addressed in order to fully realize the objectives and potentials of IoT. View full abstract»

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  • Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data

    Publication Year: 2014 , Page(s): 2 - 10
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (911 KB) |  | HTML iconHTML  

    Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This paper describes the design and implementation of Kintense and provides empirical evidence that the system is 11% - 16% more accurate and 10% - 54% more robust to changes in distance, body orientation, speed, and person when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers. We deploy Kintense in two multi-person households and demonstrate how it evolves to discover and learn unseen actions, achieves up to 90% accuracy, runs in real-time, and reduces false alarms with up to 13 times fewer user interactions than a typical system. View full abstract»

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  • Nonparametric discovery of human routines from sensor data

    Publication Year: 2014 , Page(s): 11 - 19
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    People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). With recent developments in ubiquitous sensor technologies, it becomes easier to acquire a massive amount of sensor data. One main line of research is to mine human routines from sensor data using parametric topic models such as latent Dirichlet allocation. The main shortcoming of parametric models is that it assumes a fixed, pre-specified parameter regardless of the data. Choosing an appropriate parameter usually requires an inefficient trial-and-error model selection process. Furthermore, it is even more difficult to find optimal parameter values in advance for personalized applications. In this paper, we present a novel nonparametric framework for human routine discovery that can infer high-level routines without knowing the number of latent topics beforehand. Our approach is evaluated on public datasets in two routine domains: a 34-daily-activity dataset and a transportation mode dataset. Experimental results show that our nonparametric framework can automatically learn the appropriate model parameters from sensor data without any form of model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks. View full abstract»

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  • Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

    Publication Year: 2014 , Page(s): 20 - 28
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (970 KB) |  | HTML iconHTML  

    Exploiting context from stream data in pervasive environments remains a challenge. We aim to extract proximal context from Bluetooth stream data, using an incremental, Bayesian nonparametric framework that estimates the number of contexts automatically. Unlike current approaches that can only provide final proximal grouping, our method provides proximal grouping and membership of users over time. Additionally, it provides an efficient online inference. We construct co-location matrix over time using Bluetooth data. A Poisson-exponential model is used to factorize this matrix into a factor matrix, interpreted as proximal groups, and a coefficient matrix that indicates factor usage. The coefficient matrix follows the Indian Buffet Process prior, which estimates the number of factors automatically. The non-negativity and sparsity of factors are enforced by using the exponential distribution to generate the factors. We propose a fixed-lag particle filter algorithm to process data incrementally. We compare the incremental inference (particle filter) with full batch inference (Gibbs sampling) in terms of normalized factorization error and execution time. The normalized error obtained through our incremental inference is comparable to that of full batch inference, whilst the execution time is more than 100 times faster. The discovered factors have similar meaning to the results of the popular Louvain method for community detection. View full abstract»

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  • Using rule mining to understand appliance energy consumption patterns

    Publication Year: 2014 , Page(s): 29 - 37
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1111 KB) |  | HTML iconHTML  

    Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes. View full abstract»

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  • SocketWatch: An autonomous appliance monitoring system

    Publication Year: 2014 , Page(s): 38 - 43
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    A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage. View full abstract»

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  • Personalized Energy Auditor: Estimating personal electricity usage

    Publication Year: 2014 , Page(s): 44 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (866 KB) |  | HTML iconHTML  

    The goal of energy monitoring and eco-feedback systems is to induce energy consumers to change their behaviors to achieve a more sustainable way of life. Comprehensive research and commercial solutions provide energy consumers with information about overall energy costs or appliance-level energy usage. However, in order to better promote spontaneous energy saving with an eco-feedback system, personalized information is required. The conventional solutions cannot identify the energy usage of an individual user in a shared residential environment. In this paper, we propose the Personalized Energy Auditor, which estimates personal energy usage at home. Our system monitors and analyzes appliance usage, as well as the energy cost of the daily activities of residents. The system then estimates personal energy usage automatically, by linking appliance usage data with the individual user. Our system was installed in residential homes, and the experimental results indicate that it accurately estimates personal energy usage. View full abstract»

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  • Keynote: IoT on the move: The ultimate driving machine as the ultimate mobile thing

    Publication Year: 2014 , Page(s): 50
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    Summary form only given. Automotive customers' expectations have significantly changed in the past decade. They are influenced by new technologies such as the high-bandwith mobile Internet and increasingly capable mobile consumer electronics devices. At the same time, urbanization and the necessity for ecological sustainability provoke new demands. Internet based communities are drivers for a developing Shareconomy. It is evident that that the conventional car, whether it is the all mechanical Auto 1.0 or the electronics and driving assistance systems loaded Auto 2.0, will not be able to meet these expectations and demands. The upcoming Auto 3.0 is no longer mobility hardware, but instead integral part of a seamless intermodal mobility chain for individuals. It is connected with the surrounding environment and other modes of transport. It is capable of understanding the mobility requirements of the individual, and it delivers the right information at the right time in order to make the right decisions. For this to work, the Auto 3.0 will need access to all sorts of data that is always current, comprehensive, filtered, and geographically referenced. Important enabling technologies are cross-OEM crowd sourcing of data as well as processing and real-time data analytics in the cloud. The vehicle finally becomes part of the Internet of Things. Benefits that can already today be achieved are for example traffic jam avoidance by enrichment of the navigation map with relevant real-time traffic flow information, reduction of CO2 emissions by anticipatory adaption of engines, power train and recuperation, and extension of the mileage of electric vehicles. The next steps are to open up the systems and to establish market places for the interconnection of different services. Traditionally, the Internet of Things, services realized by combining sensors, computing backends, and apps are part of a single offering and cannot be combined with other services. Platforms such as IFTT- , on the other hand, connect different ecosystems and enable a new class of services. Especially the Auto 3.0 will benefit from such an open platform which allows the implementation of multiplicity of connected and location-based services including data from numerous systems, operators, and stakeholders. Additional potentials in efficiency for future smart cities can be realized by this interconnection of different systems and participants such as vehicles, buildings, urban administration, and utilities. Establishing such a platform is an important objective. View full abstract»

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  • Multi-scale Conditional Random Fields for first-person activity recognition

    Publication Year: 2014 , Page(s): 51 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (474 KB) |  | HTML iconHTML  

    We propose a novel pervasive system to recognise human daily activities from a wearable device. The system is designed in a form of reading glasses, named `Smart Glasses', integrating a 3-axis accelerometer and a first-person view camera. Our aim is to classify user's activities of daily living (ADLs) based on both vision and head motion data. This ego-activity recognition system not only allows caretakers to track on a specific person (such as patient or elderly people), but also has the potential to remind/warn people with cognitive impairments of hazardous situations. We present the following contributions in this paper: a feature extraction method from accelerometer and video; a classification algorithm integrating both locomotive (body motions) and stationary activities (without or with small motions); a novel multi-scale dynamic graphical model structure for structured classification over time. We collect, train and validate our system on a large dataset containing 20 hours of ADLs data, including 12 daily activities under different environmental settings. Our method improves the classification performance (F-Score) of conventional approaches from 43.32%(video features) and 66.02%(acceleration features) by an average of 20-40% to 84.45%, with an overall accuracy of 90.04% in realistic ADLs. View full abstract»

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  • Inferring occupancy from opportunistically available sensor data

    Publication Year: 2014 , Page(s): 60 - 68
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    Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information. View full abstract»

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  • Pushing the spatio-temporal resolution limit of urban air pollution maps

    Publication Year: 2014 , Page(s): 69 - 77
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8496 KB) |  | HTML iconHTML  

    Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment. View full abstract»

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  • From proximity sensing to spatio-temporal social graphs

    Publication Year: 2014 , Page(s): 78 - 87
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    Understanding the social dynamics of a group of people can give new insights into social behavior. Physical proximity between individuals results from the interactions between them. Hence, measuring physical proximity is an important step towards a better understanding of social behavior. We discuss a novel approach to sense proximity from within the social dynamics. Our primary objective is to construct a spatio-temporal social graph from noisy proximity data. We address the technical and algorithmic challenges of measuring proximity reliably and accurately. Simulations and real world experiments demonstrate the feasibility and scalability of our approach. Our algorithms doubles the sensitivity of proximity detections at the cost of a slight reduction in specificity. View full abstract»

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  • A refined limit on the predictability of human mobility

    Publication Year: 2014 , Page(s): 88 - 94
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    It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiotemporal granularity down to the level of GPS data, and under a similar methodology results once again suggested a high predictability upper bound (i.e. 90% when movement was quantized down to a spatial resolution approximately the size of a large building). In this work we reconsider the derivation of the upper bound to movement predictability. By considering real-world topological constraints we are able to achieve a tighter upper bound, representing a more refined limit to the predictability of human movement. Our results show that this upper bound is between 11-24% less than previously claimed at a spatial resolution of approx. 100m×100m, with a greater improvement for finer spatial resolutions. This indicates that human mobility is potentially less predictable than previously thought. We provide an in-depth examination of how varying the spatial and temporal quantization affects predictability, and consider the impact of corresponding limits using a large set of real-world GPS traces. Particularly at fine-grained spatial quantizations, where a significant number of practical applications lie, these new (lower) upper limits raise serious questions about the use of location information alone for prediction, contributing more evidence that such prediction must integrate external variables. View full abstract»

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  • A regression-based radar-mote system for people counting

    Publication Year: 2014 , Page(s): 95 - 102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (933 KB) |  | HTML iconHTML  

    People counting is key to a diverse set of sensing applications. In this paper, we design a mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number of people within the ~10m radial range of the radar. In contrast to extant solutions, most of which use computer vision, our solution is light-weight and private. It also better tolerates the presence of obstacles that partially or fully impair line of sight; this is achieved by accounting for “small” indirect radio reflections via joint time-frequency domain features. The counter itself is realized using Support Vector Regression; the regression map is learned from a medium sized dataset of 0-~40 people in various indoor room settings. 10-fold cross validation of our counter yields a mean absolute error of 2.17 between the estimated count and the ground truth and a correlation coefficient of 0.97.We compare the performance of our solution with baseline counters. View full abstract»

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  • Missing sensor value estimation method for participatory sensing environment

    Publication Year: 2014 , Page(s): 103 - 111
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    Participatory sensing produces incomplete sensor data. Thus, we have to fill in the gaps of any missing values in the sensor data in order to provide sensor-based services. We propose a method to estimate a missing value of incomplete sensor data. It accurately estimates a missing value by repeating two processes: selecting sensors locally correlated with the sensor that includes the missing value and then updating the training sensor dataset that consist of data from the selected sensors available for multiple regression. This procedure effectively helps to find more suitable neighbor records of a query record from the training sensor dataset and to refine the regression model using the records. It overcomes three problems that other estimation methods have: a decrease in the amount of available training sensor dataset due to missing values, the difficulty in finding similar records of a query due to the “curse of dimensionality,” and the complexity in formalizing the estimation model due to “overfitting.” The main feature of our method is the way it repeatedly prunes inessential sensors while exploiting the anti-monotone property in which the training sensor dataset R' that consist of the sensors V' ⊂ V is larger than the data R that consist of V. Empirical evaluations done using public datasets in which we appended missing values show that our method increases the training sensor dataset for estimation and improves estimation accuracy through repeated sensor selections. Furthermore, we confirmed through a field trial and a life-log enrichment trial, that our method was effective for estimating missing sensor values in a participatory sensing environment. View full abstract»

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  • Capacity of pervasive camera based communication under perspective distortions

    Publication Year: 2014 , Page(s): 112 - 120
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1343 KB) |  | HTML iconHTML  

    Cameras are ubiquitous and increasingly being used not just for capturing images but also for communicating information. For example, the pervasive QR codes can be viewed as communicating a short code to camera-equipped sensors and recent research has explored using screen-to-camera communications for larger data transfers. Such communications could be particularly attractive in pervasive camera based applications, where such camera communications can reuse the existing camera hardware and also leverage from the large pixel array structure for high data-rate communication. While several prototypes have been constructed, the fundamental capacity limits of this novel communication channel in all but the simplest scenarios remains unknown. The visual medium differs from RF in that the information capacity of this channel largely depends on the perspective distortions while multipath becomes negligible. In this paper, we create a model of this communication system to allow predicting the capacity based on receiver perspective (distance and angle to the transmitter). We calibrate and validate this model through lab experiments wherein information is transmitted from a screen and received with a tablet camera. Our capacity estimates indicate that tens of Mbps is possible using a smartphone camera even when the short code on the screen images onto only 15% of the camera frame. Our estimates also indicate that there is room for at least 2.5x improvement in throughput of existing screen - camera communication prototypes. View full abstract»

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  • Unleashing the Wild Card for mobile payment

    Publication Year: 2014 , Page(s): 121 - 129
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    Mobile wallets promise a future where users do not need to carry physical payment cards. However, the slow adoption of contactless point of sales (POS) terminals by merchants limits the potential of Near-Field Communication (NFC) based payment devices. In this paper, we present Wild Card, a secure and backward compatible solution for making mobile payments at conventional magnetic stripe based POS terminals. Our solution resembles a traditional credit card in its physical dimensions and stays in the phone case. It can be programmatically set by an NFC-enabled mobile phone to any card number that the user owns. The key technologies that enable Wild Card are a fully programmable magnetic stripe, an energy harvesting system that allows the card to be charged and programmed by the phone through NFC, and a security mechanism that makes card information resilient to attacks on mobile devices. With a prototype, we evaluate the feasibility of Wild Card in terms of functionality and energy budget. View full abstract»

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  • Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning

    Publication Year: 2014 , Page(s): 130 - 138
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (14584 KB) |  | HTML iconHTML  

    The optimization of logistics in large building complexes with many resources, such as hospitals, require realistic facility management and planning. Current planning practices rely foremost on manual observations or coarse unverified assumptions and therefore do not properly scale or provide realistic data to inform facility planning. In this paper, we propose analysis methods to extract knowledge from large sets of network collected WiFi traces to better inform facility management and planning in large building complexes. The analysis methods, which build on a rich set of temporal and spatial features, include methods for noise removal, e.g., labeling of beyond building-perimeter devices, and methods for quantification of area densities and flows, e.g., building enter and exit events, and for classifying the behavior of people, e.g., into user roles such as visitor, hospitalized or employee. Spatio-temporal visualization tools built on top of these methods enable planners to inspect and explore extracted information to inform facility-planning activities. To evaluate the methods, we present results for a large hospital complex covering more than 10 hectares. The evaluation is based on WiFi traces collected in the hospital's WiFi infrastructure over two weeks observing around 18000 different devices recording more than a billion individual WiFi measurements. For the presented analysis methods we present quantitative performance results, e.g., demonstrating over 95% accuracy for correct noise removal of beyond building perimeter devices. We furthermore present detailed statistics from our analysis regarding people's presence, movement and roles, and example types of visualizations that both highlight their potential as inspection tools for planners and provide interesting insights into the test-bed hospital. View full abstract»

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  • MapGENIE: Grammar-enhanced indoor map construction from crowd-sourced data

    Publication Year: 2014 , Page(s): 139 - 147
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1616 KB) |  | HTML iconHTML  

    While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open problems: First, there is still no off-the-shelf indoor positioning system for mobile devices and, second, indoor maps are not publicly available for most buildings. While there is an extensive body of work on the first problem, the efficient creation of indoor maps remains an open challenge. We tackle the indoor mapping challenge in our MapGENIE approach that automatically derives indoor maps from traces collected by pedestrians moving around in a building. Since the trace data is collected in the background from the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of traditional indoor map creation and increases the efficiency of indoor mapping. To enhance the map building process, MapGENIE leverages exterior information about the building and uses grammars to encode structural information about the building. Hence, in contrast to existing work, our approach works without any user interaction and only needs a small amount of traces to derive the indoor map of a building. To demonstrate the performance of MapGENIE, we implemented our system using Android and a foot-mounted IMU to collect traces from volunteers. We show that using our grammar approach, compared to a purely trace-based approach we can identify up to four times as many rooms in a building while at the same time achieving a consistently lower error in the size of detected rooms. View full abstract»

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  • The telepathic phone: Frictionless activity recognition from WiFi-RSSI

    Publication Year: 2014 , Page(s): 148 - 155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6643 KB) |  | HTML iconHTML  

    We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones. View full abstract»

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