Abstract. In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Decision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers – thigh and wrist – the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves. 1

This paper presents MyExperience, a system for capturing both objective and subjective in situ data on mobile computing activities. MyExperience combines the following two techniques: 1) passive logging of device usage, user context, and environmental sensor readings, and 2) active context-triggered user experience sampling to collect in situ, subjective user feedback. MyExperience currently runs on mobile phones and supports logging of more than 140 event types, including: 1) device usage such as communication, application usage, and media capture, 2) user context such as calendar appointments, and 3) environmental sensing such as Bluetooth and GPS. In addition, user experience sampling can be targeted to moments of interest by triggering off sensor readings. We present several case studies of field deployments on people’s personal phones to demonstrate how MyExperience can be used effectively to understand how people use and experience mobile technology.

Abstract. Three tools for acquiring data about people, their behavior, and their use of technology in natural settings are described: (1) a context-aware experience sampling tool, (2) a ubiquitous sensing system that detects environmental changes, and (3) an image-based experience sampling system. We discuss how these tools provide researchers with a flexible toolkit for collecting data on activity in homes and workplaces, particularly when used in combination. We outline several ongoing studies to illustrate the versatility of these tools. Two of the tools are currently available to other researchers to use. 1

this article was aided by National Institutes of Health Grant MH52391 and by a scholarship from the German National Scholarship Foundation. We thank David Buss, Kenneth Craik, Samuel Gosling, Anna Graybeal, and Sean Massey for their helpful comments on previous versions of this article. We are indebted to Geetha Desikan, Kim Fisher, Beth Henary, Allison McClure, John McFarland, Kristy Orr, Nathalie Shook, Monica Sidarous, Mary-Beth Sylvester, and Sara Webb for their help in collecting the data and transcribing the audiotapes

As an increasing number of new technologies are turning a strong focus on health assessment applications, new engineering and design challenges emerge. Challenges such as inference, modeling, data mining, and feedback for long-term usage arise. This paper argues that embedding empathy into the design of these interactive systems can potentially be vital in the acceptance and success of these types of technologies. This paper discusses three pieces of work that illustrate that designing systems that are intentionally empathetic can play a significant role in creating a better user experience in human-computer interactions. 1.

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Advances in digital technologies for information capture combined with massive increases in the capacity of digital storage media mean that it is now possible to capture and store one’s entire life experiences in a Human Digital Memory (HDM). Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices. These diverse collections of personal information are potentially very valuable, but will only be so if significant information can be reliably retrieved from them. HDMs differ from traditional document collections for which existing search technologies have been developed since users may have poor recollection of contents or even the existence of stored items. Additionally HDM data is highly heterogeneous and unstructured, making it difficult to form search queries. We believe that a Personal Information Management (PIM) system which exploits the context of information capture, and potentially of earlier refinding, can be valuable in effective retrieval from an HDM. We report an investigation into how individuals perform searches of their personal information, and use the outcome of this study to develop an information retrieval (IR) framework for HDM search incorporating the context of document capture. We then describe the creation of a pilot HDM test collection, and initial experiments in retrieval from this collection. Results from these experiments indicate that use of context data can be significantly beneficial to increasing the efficient retrieval of partially recalled items from an HDM.

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user’s health related habits and observations.

As users store and share more digital content at home, access control becomes increasingly important. One promising approach for helping non-expert users create accurate access policies is reactive policy creation, in which users can update their policy dynamically in response to access requests that would not otherwise succeed. An earlier study suggested reactive policy creation might be a good fit for file access control at home. To test this, we conducted an experience-sampling study in which participants used a simulated reactive access-control system for a week. Our results bolster the case for reactive policy creation as one mode by which home users specify access-control policy. We found both quantitative and qualitative evidence of dynamic, situational policies that are hard to implement using traditional models but that reactive policy creation can facilitate. While we found some clear disadvantages to the reactive model, they do not seem insurmountable.

This article is an editorial note submitted to CCR. It has NOT been peer reviewed. The authors take full responsibility for this article’s technical content. Comments can be posted through CCR Online. There is an amazing paucity of data that is collected directly from users ’ personal computers. One key reason for this is the perception among researchers that users are unwilling to participate in such a data collection effort. To understand the range of opinions on matters that occur with end-host data tracing, we conducted a survey of 400 computer scientists. In this paper, we summarize and share our findings.