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

Abstract. A motivation behind much UbiComp research has been to make our lives convenient, comfortable and informed, following in the footsteps of Weiser’s calm computing vision. Three themes that have dominated are context awareness, ambient intelligence and monitoring/tracking. While these avenues of research have been fruitful their accomplishments do not match up to anything like Weiser’s world. This paper discusses why this is so and argues that is time for a change of direction in the field. An alternative agenda is outlined that focuses on engaging rather than calming people. Humans are very resourceful at exploiting their environments and extending their capabilities using existing strategies and tools. I describe how pervasive technologies can be added to the mix, outlining three areas of practice where there is much potential for professionals and laypeople alike to combine, adapt and use them in creative and constructive ways.

We introduce the PlaceLab, a new “living laboratory ” for the study of ubiquitous technologies in home settings. The PlaceLab is a tool for researchers developing context-aware and ubiquitous interaction technologies. It complements more traditional data gathering instruments and methods, such as home ethnography and laboratory studies. We describe the data collection capabilities of the laboratory and current examples of its use. Author Keywords Ubiquitous computing, context-aware, living laboratory, home, sensors, research methods, ethnography. ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI):

This paper presents a framework for power-efficient detection in embedded sensor systems. State detection is structured as a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured, parameterized fashion. An embedded instantiation of these classifiers, including real-time sensor control, is described. An application based on a wearable gait monitor provides quantitative support for this framework. The decision tree classifiers achieved roughly identical detection accuracies to those obtained using support vector machines while drawing three times less power. Both simulation and real-time operation of the classifiers demonstrate that our multi-tiered classifier determines states as accurately as a single-trigger (binary) wakeup system while drawing as little as half as much power and with only a negligible increase in latency.

If current trends in cellular phone technology, personal digital assistants, and wireless networking are indicative of the future, we can expect our environments to contain an abundance of networked computational devices and resources. We envision these devices acting in an orchestrated manner to meet users ’ needs, pushing the level of interaction away from particular devices and towards interactions with the environment as a whole. Computation will be based not only on input explicitly provided by the user, but also on contextual information passively collected by networked sensing devices. Configuring the desired responses to different situations will need to be easy for users. However, we anticipate that the triggering situations for many desired automation policies will be complex, unforeseen functions of low-level contextual information. This is problematic since users, though easily able to perceive triggering situations, will not be able to define them as functions of the devices ’ available contextual information, even when such a function (or a close approximation) does exist. In this paper, we present an alternative approach for specifying the automation rules of a pervasive computing environment using machine learning techniques. Using this approach, users generate training data for an automation policy through demonstration, and, after training is completed, a learned function is employed for future automation. This approach enables users to automate the environment based on changes in the environment that are complex, unforeseen combinations of contextual information. We developed our learning service within Gaia, our pervasive computing system, and deployed it within our prototype pervasive computing environment. Using the system, we were able to have users demonstrate how sound and lighting controls should adjust to different applications used within the environment, the users present, and the locations of those users and then automate those demonstrated preferences. 1.

This is to certify that I have examined this copy of a doctoral dissertation by

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and my sons, Matthew and Michael Riley. iii ACKNOWLEDGEMENTS I especially thank the members of my committee for your time and committment, Dr.

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