Interaction in Augmented Reality or Mixed Reality environments is generally classified into two modalities: linear (relative to object) or non-linear (relative to camera). Switching between these modes can be arduous in cases where someone's interaction with the device is limited or restricted as is often the case in medical or industrial applications where one's hands might be sterile or soiled. To solve this, we present Sound-to-Experience where the modality can be effectively toggled by a noise or sound which is detected using a modern Artificial Intelligence deep-network classifier.

Accurate localization is a fundamental requirement for a variety of applications, ranging from industrial robot operations to location-powered applications on mobile devices. A key technical challenge in achieving this goal is providing a clean and reliable estimation of location from a variety of low-cost, uncalibrated sesnors. Many current techniques rely on Particle Filter (PF) based algorithms. They have proven successful at effectively fusing various sensors inputs to create meaningful location predictions. In this paper we build upon this large corpous of work. Like prior work, our technique fuses Received Signal Strength Indicator (RSSI) measurements from Bluetooth Low Energy (BLE) beacons with map information. A key contribution of our work is a new sensor model for BLE beacons that does not require the mapping from RSSI to distance. We further contribute a novel method of utilizing map information during the initialization of the system and during the resampling phase when new particles are generated. Using our proposed sensor model and map prior information the performance of the overall localization is improved by 1.20 m on comparing the 75th percentile of the cumulative distribution with traditional localization techniques.

In this paper, we develop a system for the lowcost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time varying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.

Devices with embedded sensors are permeating the computing landscape, allowing the collection and analysis of rich data about individuals, smart spaces, and their interactions. This class of de- vices enables a useful array of home automation and connected workplace functionality to individuals within instrumented spaces. Unfortunately, the increasing pervasiveness of sensors can lead to perceptions of privacy loss by their occupants. Given that many instrumented spaces exist as platforms outside of a user’s control—e.g., IoT sensors in the home that rely on cloud infrastructure or connected workplaces managed by one’s employer—enforcing access controls via a trusted reference monitor may do little to assuage individuals’ privacy concerns. This calls for novel enforcement mechanisms for controlling access to sensed data. In this paper, we investigate the interplay between sensor fidelity and individual comfort, with the goal of understanding the design space for effective, yet palatable, sensors for the workplace. In the context of a common space contextualization task, we survey and interview individuals about their comfort with three common sensing modalities: video, audio, and passive infrared. This allows us to explore the extent to which discomfort with sensor platforms is a function of detected states or sensed data. Our findings uncover interesting interplays between content, context, fidelity, history, and privacy. This, in turn, leads to design recommendations regarding how to increase comfort with sensing technologies by revisiting the mechanisms by which user preferences and policies are enforced in situations where the infrastructure itself is not trusted.

In this paper, we propose a novel solution to optimize the deployment of (RF) beacons for the purpose of indoor localization. We propose a system that optimizes both the number of beacons and their placement in a given environment. We propose a novel cost-function, called CovBSM, that allows to simultaneously optimize the 3-coverage while maximizing the beacon spreading. Using this cost function, we propose a framework that maximize both the number of beacons and their placement in a given environment. The proposed solution accounts for the indoor infrastructure and its influence on the (RF) signal propagation by embedding a realistic simulator into the optimization process.

モバイル技術の発展と日常生活における継続的なつながりは、仕事の進め方に大きく影響を与えている。センシング技術の活用は個人による使用事例が多くを占めているが、ワークプレイスはセンシング技術を活用するのに重要かつ適切な環境である。つまり、従業員が自分の追跡可能な端末を使ってセンシング技術を連携させることが可能である。本稿では、ワークプレイスにおける身体的、精神的、および社会的に良好な状態と生産性を向上させる技術について、2つの最新の調査結果と、行動を変える姿勢を維持 るための仕組みを報告する。次に、新しい作業の領域について簡単に議論する。

Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace.

In recent years, there has been an explosion of services that lever- age location to provide users novel and engaging experiences. However, many applications fail to realize their full potential because of limitations in current location technologies. Current frameworks work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level indoor location. LoCo does not require users to carry specialized location hardware—it uses radios that are present in most contemporary devices and, combined with a boosting classification technique, provides a significant runtime performance improvement. We provide experiments that show the combined radio technique can achieve accuracy that improves on current state-of-the-art Wi-Fi only techniques. LoCo is designed to be easily deployed within an environment and readily leveraged by application developers. We believe LoCo’s high accuracy and accessibility can drive a new wave of location-driven applications and services.

New technology comes about in a number of different ways. It may come from advances in scientific research, through new combinations of existing technology, or by simply from imagining what might be possible in the future. This video describes the evolution of Tabletop Telepresence, a system for remote collaboration through desktop videoconferencing combined with a digital desk. Tabletop Telepresence provides a means to share paper documents between remote desktops, interact with documents and request services (such as translation), and communicate with a remote person through a teleconference. It was made possible by combining advances in camera/projector technology that enable a fully functional digital desk, embodied telepresence in video conferencing and concept art that imagines future workstyles.

Location-enabled applications now permeate the mobile computing landscape. As technologies like Bluetooth Low Energy (BLE) and Apple's iBeacon protocols begin to see widespread adoption, we will no doubt see a proliferation of indoor location enabled application experiences. While not essential to each of these applications, many will require that the location of the device be true and verifiable. In this paper, we present LocAssure, a new framework for trusted indoor location estimation. The system leverages existing technologies like BLE and iBeacons, making the solution practical and compatible with technologies that are already in use today. In this work, we describe our system, situate it within a broad location assurance taxonomy, describe the protocols that enable trusted localization in our system, and provide an analysis of early deployment and use characteristics. Through developer APIs, LocAssure can provide critical security support for a broad range of indoor location applications.

As video-mediated communication reaches broad adoption, improving immersion and social interaction are important areas of focus in the design of tools for exploration and work-based communication. Here we present three threads of research focused on developing new ways of enabling exploration of a remote environment and interacting with the people and artifacts therein.

In this paper, we report findings from a study that compared basic video-conferencing, emergent kinetic video-conferencing techniques, and face-to-face meetings. In our study, remote and co-located participants worked together in groups of three. We show, in agreement with prior literature, the strong adverse impact of being remote on participation-levels. We also show that local and remote participants perceived differently their own contributions and others. Extending prior work, we also show that local participants exhibited significantly more overlapping speech with remote participants who used an embodied proxy, than with remote participants in basic-video conferencing (and at a rate similar to overlapping speech for co-located groups). We also describe differences in how the technologies were used to follow conversation. We discuss how these findings extend our understanding of the promise and potential limitations of embodied video-conferencing solutions.

In recent years, there has been an explosion of social and collaborative applications that leverage location to provide users novel and engaging experiences. Current location technologies work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level location using a supervised classification scheme. We provide experiments that show this technique is orders of magnitude more efficient than current state-of-the-art Wi- Fi localization techniques. Low classification overhead and computational footprint make classification practical and efficient even on mobile devices. Our framework has also been designed to be easily deployed and lever- aged by developers to help create a new wave of location- driven applications and services.

Collaboration technologies must support information sharing between collaborators, but must also take care not to share too much information or share information too widely. Systems that share information without requiring an explicit action by a user to initiate the sharing must be particularly cautious in this respect. Presence systems are an emerging class of applications that support collaboration. Through the use of pervasive sensors, these systems estimate user location, activities, and available communication channels. Because such presence data are sensitive, to achieve wide-spread adoption, sharing models must reflect the privacy and sharing preferences of their users. This paper looks at the role that privacy-preserving aggregation can play in addressing certain user sharing and privacy concerns with respect to presence data. We define conditions to achieve CollaPSE (Collaboration Presence Sharing Encryption) security, in which (i) an individual has full access to her own data, (ii) a third party performs computation on the data without learning anything about the data values, and (iii) people with special privileges called “analysts” can learn statistical information about groups of individuals, but nothing about the individual values contributing to the statistic other than what can be deduced from the statistic. More specifically, analysts can decrypt aggregates without being able to decrypt the individual values contributing to the aggregate. Based in part on studies we carried out that illustrate the need for the conditions encapsulated by CollaPSE security, we designed and implemented a family of CollaPSE protocols. We analyze their security, discuss efficiency tradeoffs, describe extensions, and review more recent privacy-preserving aggregation work.

We describe Explorer, a system utilizing mirror worlds - dynamic 3D virtual models of physical spaces that reflect the structure and activities of those spaces to help support navigation, context awareness and tasks such as planning and recollection of events. A rich sensor network dynamically updates the models, determining the position of people, status of rooms, or updating textures to reflect displays or bulletin boards. Through views on web pages, portable devices, or on 'magic window' displays located in the physical space, remote people may 'Clook in' to the space, while people within the space are provided with augmented views showing information not physically apparent. For example, by looking at a mirror display, people can learn how long others have been present, or where they have been. People in one part of a building can get a sense of activities in the rest of the building, know who is present in their office, and look in to presentations in other rooms. A spatial graph is derived from the 3D models which is used both to navigational paths and for fusion of acoustic, WiFi, motion and image sensors used for positioning. We describe usage scenarios for the system as deployed in two research labs, and a conference venue.

Affect influences workplace collaboration and thereby impacts a workplace's productivity. Participants in face-toface interactions have many cues to each other's affect, but work is increasingly carried out via computer-mediated channels that lack many of these cues. Current presence systems enable users to estimate the availability of other users, but not their affect states or communication preferences. This work investigates relationships between affect state and communication preferences and demonstrates the feasibility of estimating affect state and communication preferences from a presence state stream.

Poster

Modern office work practices increasingly breach traditional boundaries of time and place, making it difficult to interact with colleagues. To address these problems, we developed myUnity, a software and sensor platform that enables rich workplace awareness and coordination. myUnity is an integrated platform that collects information from a set of independent sensors and external data aggregators to report user location, availability, tasks, and communication channels. myUnity's sensing architecture is component-based, allowing channels of awareness information to be added, updated, or removed at any time. Multiple channels of input are combined and composited into a single, high-level presence state. Early studies of a myUnity deployment have demonstrated that the platform allows quick access to core awareness information and show that it has become a useful tool for supporting communication and collaboration in the modern workplace.

Modern office work practices increasingly breach traditional boundaries of time and place, making it difficult to interact with colleagues. To address these problems, we developed myUnity, a software and sensor platform that enables rich workplace awareness and coordination. myUnity is an integrated platform that collects information from a set of independent sensors and external data aggregators to report user location, availability, tasks, and communication channels. myUnity's sensing architecture is component-based, allowing channels of awareness information to be added, updated, or removed at any time. Our current system includes a variety of sensor and data input, including camera-based activity classification, wireless location trilateration, and network activity monitoring. These and other input channels are combined and composited into a single, high-level presence state. Early studies of a myUnity deployment have demonstrated that use of the platform allows quick access to core awareness information and show it has become a useful tool supporting communication and collaboration in the modern workplace.

Modern office work practices increasingly breach traditional boundaries of time and place, increasing breakdowns workers encounter when coordinating interaction with colleagues. We conducted interviews with 12 workers and identified key problems introduced by these practices. To address these problems we developed myUnity, a fully functional platform enabling rich workplace awareness and coordination. myUnity is one of the first integrated platforms to span mobile and desktop environments, both in terms of access and sensing. It uses multiple sources to report user location, availability, tasks, and communication channels. A pilot field study of myUnity demonstrated the significant value of pervasive access to workplace awareness and communication facilities, as well as positive behavioral change in day-to-day communication practices for most users. We present resulting insights about the utility of awareness technology in flexible work environments.

As sensors become ever more prevalent, more and more information will be collected about each of us. A longterm research question is how best to support beneficial uses while preserving individual privacy. Presence systems are an emerging class of applications that support collaboration. These systems leverage pervasive sensors to estimate end-user location, activities, and available communication channels. Because such presence data are sensitive, to achieve wide-spread adoption, sharing models must reflect the privacy and sharing preferences of the users. To reflect users' collaborative relationships and sharing desires, we introduce CollaPSE security in which an individual has full access to her own data, a third party processes the data without learning anything about the data values, and users higher up in the hierarchy learn only statistical information about the employees under them. We describe simple schemes that efficiently realize CollaPSE security for time series data. We implemented these protocols using readily available cryptographic functions, and integrated the protocols with FXPAL's MyUnity presence system.

The massive amounts of information that are being collected about each of us will only increase as sensors become ever cheaper and more powerful. Analysis of this wealth of data supports advances in medicine and public health, improved software and services through user pattern analysis, and more efficient economic mechanisms. At the same time, the potential for misuse of such data is significant. A long-term research question is how best to support beneficial uses while inhibiting misuse. One approach is to enable individuals to maintain tighter control of their own data while still supporting the computation of group statistics. Currently, analysts are usually given access to all data in order to compute statistics, and often use a third party service provider to store, or even process, such data. Either the third party has access to all data or the data are encrypted, in which case the third party does no processing. An interesting research question is how to provide mechanisms to support "need to know" security in which an individual has full access to her own data, the third party learns nothing about the data but can nevertheless contribute to the processing, and the analyst learns only the desired statistics. We have explored "need to know" security in connection with MyUnity, a prototype awareness system. MyUnity collects data from a variety of sources and displays summary presence states, such as ``in office'' or ``with visitor,'' computed from the received data. MyUnity was deployed in a small research lab and has been in use by over 30 people for more than a year. To avoid concerns about misuse, the system did not store any data initially. The researchers developing the system were interested, however, in analyzing usage patterns, and users expressed interest in seeing personal trends, activity patterns of coworkers, and long-term data pooled across groups of users, all requiring data to be stored. At the same time, users continued to be concerned about misuse of stored data. We looked at ``need to know'' security for cases in which, at each time step, each member of a group of users has a value (i.e., a presence state) to contribute, and the group would like to provide only an aggregate view of those values to people outside their group. We designed and implemented an efficient protocol that enables each user to encrypt under her own key in such a way that a third party can compute an encryption of a sum across values encrypted under different keys without the need for further interactions with the individuals. The protocol provides means for an analyst to decrypt the encrypted sum. We designed key structures and extensions to provide a family of efficient non-interactive ``need to know'' protocols for time series data in which the analyst learns only the statistics, not the individual data values, and the third party learns nothing about the values.

The use of whiteboards is pervasive across a wide range of work domains. But some of the qualities that make them successful—an intuitive interface, physical working space, and easy erasure—inherently make them poor tools for archival and reuse. If whiteboard content could be made available in times and spaces beyond those supported by the whiteboard alone, how might it be appropriated? We explore this question via ReBoard, a system that automatically captures whiteboard images and makes them accessible through a novel set of user-centered access tools. Through the lens of a seven week workplace field study, we found that by enabling new workflows, ReBoard increased the value of whiteboard content for collaboration.

The modern workplace is inherently collaborative, and this collaboration relies on effective communication among coworkers. Many communication tools – email, blogs, wikis, Twitter, etc. – have become increasingly available and accepted in workplace communications. In this paper, we report on a study of communications technologies used over a one year period in a small US corporation. We found that participants used a large number of communication tools for different purposes, and that the introduction of new tools did not impact significantly the use of previously-adopted technologies. Further, we identified distinct classes of users based on patterns of tool use. This work has implications for the design of technology in the evolving ecology of communication tools.

In certain applications such as radiology and imagery analysis, it is important to minimize errors. In this paper we evaluate a structured inspection method that uses eye tracking information as a feedback mechanism to the image inspector. Our two-phase method starts with a free viewing phase during which gaze data is collected. During the next phase, we either segment the image, mask previously seen areas of the image, or combine the two techniques, and repeat the search. We compare the different methods proposed for the second search phase by evaluating the inspection method using true positive and false negative rates, and subjective workload. Results show that gaze-blocked configurations reduced the subjective workload, and that gaze-blocking without segmentation showed the largest increase in true positive identifications and the largest decrease in false negative identifications of previously unseen objects.