With the tremendous progress in sensing and IoT infrastructure, it is foreseeable that IoT systems will soon be available for commercial markets, such as in people's homes. In this paper, we present a deployment study using sensors attached to household objects to capture the resourcefulness of three individuals. The concept of resourcefulness highlights the ability of humans to repurpose objects spontaneously for a different use case than was initially intended. It is a crucial element for human health and wellbeing, which is of great interest for various aspects of HCI and design research. Traditionally, resourcefulness is captured through ethnographic practice. Ethnography can only provide sparse and often short duration observations of human experience, often relying on participants being aware of and remembering behaviours or thoughts they need to report on. Our hypothesis is that resourcefulness can also be captured through continuously monitoring objects being used in everyday life. We developed a system that can record object movement continuously and deployed them in homes of three elderly people for over two weeks. We explored the use of probabilistic topic models to analyze the collected data and identify common patterns.

Despite reflection being identified as a key component of behavior change, most existing tools do not explicitly design for it, carrying an implicit assumption that providing access to self-tracking data is enough to trigger reflection. In this work we design a system for reflection around physical activity. Through a set of workshops, we generated a corpus of 275 reflective questions. We then combine these questions into a set of 25 reflective mini-dialogues. We deliver our mini-dialogues through MMS. 33 active users of fitness trackers used our system in a 2-week field deployment. Results suggest that the mini-dialogues were successful in triggering reflection and that this reflection led to increases in motivation, empowerment, and adoption of new behaviors. Encouragingly, 16 participants elected to use the system for two additional weeks without compensation. We present implications for the design of technology-supported dialog system for reflection.

Continuous monitoring with unobtrusive wearable social sensors is becoming a popular method to assess individual affect states and team effectiveness in human research. A large number of applications have demonstrated the effectiveness of applying wearable sensing in corporate settings; for example, in short periodic social events or in a university campus. However, little is known of how we can automatically detect individual affect and group cohesion for long duration missions. Predicting negative affect states and low cohesiveness is vital for team missions. Knowing team members’ negative states allows timely interventions to enhance their effectiveness. This work investigates whether sensing social interactions and individual behaviors with wearable sensors can provide insights into assessing individual affect states and group cohesion. We analyzed wearable sensor data from a team of six crew members who were deployed on a four-month simulation of a space exploration mission at a remote location. Our work proposes to recognize team members’ affect states and group cohesion as a binary classification problem using novel behavior features that represent dyadic interaction and individual activities. Our method aggregates features from individual members into group levels to predict team cohesion. Our results show that the behavior features extracted from the wearable social sensors provide useful information in assessing personal affect and team cohesion. Group task cohesion can be predicted with a high performance of over 0.8 AUC. Our work demonstrates that we can extract social interactions from sensor data to predict group cohesion in longitudinal missions. We found that quantifying behavior patterns including dyadic interactions and face-to-face communications are important in assessing team process.

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.

In this chapter we discuss the use of external sources of data in designing conversational dialogues. We focus on applications in behavior change around physical activity involving dialogues that help users better understand their self-tracking data and motivate healthy behaviors. We start by introducing the areas of behavior change and personal informatics and discussing the importance of self-tracking data in these areas. We then introduce the role of reflective dialogue-based counseling systems in this domain, discuss specific value that self-tracking data can bring, and how it can be used in creating the dialogues. The core of the chapter focuses on six practical examples of design of dialogues involving self-tracking data that we either tested in our research or propose as future directions based on our experiences. We end the chapter by discussing how the design principles for involving external data in conversations can be applied to broader domains. Our goal for this chapter is to share our experiences, outline design principles, highlight several design opportunities in external data-driven computer-based conversations, and encourage the reader to explore creative ways of involving external sources of data in shaping dialogues-based interactions.

We introduce a system to automatically manage photocopies made from copyrighted printed materials. The system monitors photocopiers to detect the copying of pages from copyrighted publications. Such activity is tallied for billing purposes. Access rights to the materials can be checked to prevent printing. Digital images of the copied pages are checked against a database of copyrighted pages. To preserve the privacy of the copying of non-copyright materials, only digital fingerprints are submitted to the image matching service. A problem with such systems is creation of the database of copyright pages. To facilitate this, our system maintains statistics of clusters of similar unknown page images along with copy sequence. Once such a cluster has grown to a sufficient size, a human inspector can determine whether those page sequences are copyrighted. The system has been tested with 100,000s of pages from conference proceedings and with millions of randomly generated pages. Retrieval accuracy has been around 99% even with copies of copies or double-page copies.

Historically, people have interacted with companies and institutions through telephone-based dialogue systems and paper-based forms. Now, these interactions are rapidly moving to web- and phone-based chat systems. While converting traditional telephone dialogues to chat is relatively straightforward, converting forms to conversational interfaces can be challenging. In this work, we introduce methods and interfaces to enable the conversion of PDF and web-based documents that solicit user input into chat-based dialogues. Document data is first extracted to associate fields and their textual descriptions using meta-data and lightweight visual analysis. The field labels, their spatial layout, and associated text are further analyzed to group related fields into natural conversational units. These correspond to questions presented to users in chat interfaces to solicit information needed to complete the original documents and downstream processes they support. This user supplied data can be inserted into the source documents and/or in downstream databases. User studies of our tool show that it streamlines form-to-chat conversion and produces conversational dialogues of at least the same quality as a purely manual approach.

SlideDiff is a system that automatically creates an animated rendering of textual and media differences between two versions of a slide. While previous work focuses either on textual or image data, SlideDiff integrates text and media changes, as well as their interactions, e.g. adding an image forces nearby text boxes to shrink. Provided with two versions of a slide (not the full history of edits), SlideDiff detects the textual and image differences, and then animates the changes by mimicking what a user would have done, such as moving the cursor, typing text, resizing image boxes, adding images. This editing metaphor is well known to most users, helping them better understand what has changed, and fosters a sense of connection between remote workers, making them feel as if we edited together. After detection of text and image differences, the animations are rendered in HTML and CSS, including mouse cursor motion, text and image box selection and resizing, text deletion and insertion with its cursor. We discuss strategies for animating changes, in particular the importance of starting with large changes and finishing with smaller edits, and provide evidence of the utility of SlideDiff in a workplace setting.

Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.

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.

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos {\em across} multiple courses and content platforms to provide a choice of perspectives on topics. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.

An enormous amount of conversation occurs online every day, including on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate overlapping messages into detached conversations. In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese Hierarchical Convolutional Neural Network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for Conversation Identification by SImilarity Ranking (CISIR) then derives conversations based on high-confidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.

Conversational agents stand to play an important role in supporting behavior change and well-being in many domains. With users able to interact with conversational agents through both text and voice, understanding how designing for these channels supports behavior change is important. To begin answering this question, we designed a conversational agent for the workplace that supports workers’ activity journaling and self-learning through reflection. Our agent, named Robota, combines chat-based communication as a Slack Bot and voice interaction through a personal device using a custom Amazon Alexa Skill. Through a 3-week controlled deployment, we examine how voice-based and chat-based interaction affect workers’ reflection and support self-learning. We demonstrate that, while many current technical limitations exist, adding dedicated mobile voice interaction separate from the already busy chat modality may further enable users to step back and reflect on their work. We conclude with discussion of the implications of our findings to design of workplace self-tracking systems specifically and to behavior-change systems in general.

Convolutional Neural Networks (CNN) have successfully been utilized for localization using a single monocular image [1]. Most of the work to date has either focused on reducing the dimensionality of data for better learning of parameters during training or on developing different variations of CNN models to improve pose estimation. Many of the best performing works solely consider the content in a single image, while the context from historical images is ignored. In this paper, we propose a combined CNN-LSTM which is capable of incorporating contextual information from historical images to better estimate the current pose. Experimental results achieved using a dataset collected in an indoor office space improved the overall system results to 0.8 m & 2.5° at the third quartile of the cumulative distribution as compared with 1.5 m & 3.0° achieved by PoseNet [1]. Furthermore, we demonstrate how the temporal information exploited by the CNN-LSTM model assists in localizing the robot in situations where image content does not have sufficient features.

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.

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. This paper presents MOOCex, a technique that can offer a more flexible learning experience for MOOCs. MOOCex can recommend lecture videos across different courses with multiple perspectives, and considers both the video content and also sequential inter-topic relationships mined from course syllabi. MOOCex is also equipped with interactive visualization allowing learners to explore the semantic space of recommendations within their current learning context. The results of comparisons to traditional methods, including content-based recommendation and ranked list representation, indicate the effectiveness of MOOCex. Further, feedback from MOOC learners and instructors suggests that MOOCex enhances both MOOC-based learning and teaching.

Understanding team communication and collaboration patterns is critical for improving work efficiency in organizations. This paper presents an interactive visualization system, T-Cal, that supports the analysis of conversation data from modern team messaging platforms (e.g., Slack). T-Cal employs a user-familiar visual interface, a calendar, to enable seamless multi-scale browsing of data from different perspectives. T-Cal also incorporates a number of analytical techniques for disentangling interleaving conversations, extracting keywords, and estimating sentiment. The design of T-Cal is based on an iterative user-centered design process including field studies, requirements gathering, initial prototypes demonstration, and evaluation with domain users. The resulting two case studies indicate the effectiveness and usefulness of T-Cal in real-world applications, including student group chats during a MOOC and daily conversations within an industry research lab.

This paper describes the development of a multi-sensory clubbing experience which was deployed during two a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders.

Effective communication of activities and progress in the workplace is crucial for the success of many modern organizations. In this paper, we extend current research on workplace communication and uncover opportunities for technology to support effective work activity reporting. We report on three studies: With a survey of 68 knowledge workers followed by 14 in-depth interviews, we investigated the perceived benefits of different types of progress reports and an array of challenges at three stages: Collection, Composition, and Delivery. We show an important interplay between written and face-to-face reporting, and highlight the importance of tailoring a report to its audience. We then present results from an analysis of 722 reports composed by 361 U.S.-based knowledge workers, looking at the influence of the audience on a report’s language. We conclude by discussing opportunities for future technologies to assist both employees and managers in collecting, interpreting, and reporting progress in the workplace.

Activity recognition is a core component of many intelligent and context-aware systems. In this paper, we present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in two work domains; recognizing work activities at a convenience-store counter (useful for post-hoc analytics) and recognizing common office deskwork activities (useful for real-time applications). We classify seven clerk activities with 94.9% accuracy using data collected in a lab environment, and recognize six common deskwork activities collected in real offices with 95.3% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems.

This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, \textit{SeqSense}, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the surrounding of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to refocus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanation.

Tutorials are one of the most fundamental means of conveying knowledge. Ideally when the task involves physical or digital objects, tutorials not only describe each step with text or via audio narration but show it as well using photos or animation. In most cases, online tutorial authors capture media from handheld mobile devices to compose these documents, but increasingly they use wearable devices as well. In this work, we explore the full life-cycle of online tutorial creation and viewing using head-mounted capture and displays. We developed a media-capture tool for Google Glass that requires minimal attention to the capture device and instead allows the author to focus on creating the tutorial's content rather than its capture. The capture tool is coupled with web-based authoring tools for creating annotatable videos and multimedia documents. In a study comparing standalone (camera on tripod) versus wearable capture (Google Glass) as well as two types of multimedia representation for authoring tutorials (video-based or document-based), we show that tutorial authors have a preference for wearable capture devices, especially when recording activities involving larger objects in non-desktop environments. Authors preferred document-based multimedia tutorials because they are more straightforward to compose and the step-based structure translates more directly to explaining a procedure. In addition, we explored using head-mounted displays (Google Glass) for accessing tutorials in comparison to lightweight computing devices such as tablets. Our study included tutorials recorded with the same capture methods as in our access study. We found that although authors preferred head-mounted capture, tutorial consumers preferred video recorded by a camera on tripod that provides a more stable image of the workspace. Head-mounted displays are good for glanceable information, however video demands more attention and our participants made more errors using Glass than when using a tablet, which was easier to ignore. Our findings point out several design implications for online tutorial authoring and access methods.

Advances in small and low power electronics have created new opportunities for the Internet of Things (IoT), leading to an explosion of physical objects being connected to the Internet. However, there still lacks an indoor localization solution that can answer the needs of various location-based IoT applications with desired simplicity, robustness, accuracy, and responsiveness. We introduce Foglight, a visible light enabled indoor localization system for IoT devices that relies on unique spatial encoding produced when mechanical mirrors inside a projector are flipped based on gray-coded binary images. Foglight employs simple off-the-shelf light sensors that can be easily coupled with existing IoT devices - such as thermometers, gas meters, or light switches - making their location discoverable. Our sensor unit is computation efficient; it can perform high-accuracy localization with minimum signal processing overhead, allowing any low-power IoT device on which it rests to be able to locate itself. Additionally, results from our evaluation reveal that Foglight can locate a target device with an average accuracy of 1.7 millimeters and average refresh rate of 84 Hz with minimal latency, 31.46 milliseconds on WiFi and 23.2 milliseconds on serial communication. Two example applications are developed to demonstrate possible scenarios as proof of concept. We also discuss limitations, how they could be overcome, and propose next steps.