2011 15th Annual International Symposium on Wearable Computers

12-15 June 2011

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  • [Front cover]

    Publication Year: 2011, Page(s): C1
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  • [Title page i]

    Publication Year: 2011, Page(s): i
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  • [Title page iii]

    Publication Year: 2011, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011, Page(s): iv
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  • Table of contents

    Publication Year: 2011, Page(s):v - vii
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  • Message from the General Chair

    Publication Year: 2011, Page(s): viii
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  • Message from the Program Chairs

    Publication Year: 2011, Page(s): ix
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  • Organizing Committee

    Publication Year: 2011, Page(s): x
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  • Program Committee

    Publication Year: 2011, Page(s): xi
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  • Guest Reviewers

    Publication Year: 2011, Page(s): xii
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  • AirTouch: Synchronizing In-air Hand Gesture and On-body Tactile Feedback to Augment Mobile Gesture Interaction

    Publication Year: 2011, Page(s):3 - 10
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4224 KB) | HTML iconHTML

    We present the design and evaluation of Air Touch, a wristwatch interface that enables mobile gesture interaction through tactile feedback during limited visual attention conditions. Unlike its predecessor, the Gesture Watch, Air Touch is supported by a push-to-gesture mechanism (PTG) where the user performs a gesture and then confirms it afterward with a trigger gesture. The Gesture Watch, in con... View full abstract»

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  • A Method of Measuring Garment Movement for Wearable Sensing

    Publication Year: 2011, Page(s):11 - 14
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (185 KB) | HTML iconHTML

    This paper presents a novel method for measuring the movement of a garment relative to the body surface. Quantifying this movement affords a measurement of the displacement of a hypothetical body sensor embedded in a garment, which can be expressed as "error" or "movement noise" in some body-sensing applications. We present the method, based on motion-capture of garments worn by an animatronic run... View full abstract»

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  • How Should a Wearable Rowing Trainer Look Like? A User Study

    Publication Year: 2011, Page(s):15 - 18
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1350 KB) | HTML iconHTML

    We describe a user study investigating how a wearable training system for rowing should look like. The study covers all design stages from initial observations up to mockup user tests. In contrast to other work concentrating mainly on professional rowers, we target the much larger group of beginners. For our study we had a test group of 80 students and instructors who were observed for 25 hours, w... View full abstract»

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  • GroupUs: Smartphone Proximity Data and Human Interaction Type Mining

    Publication Year: 2011, Page(s):21 - 28
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (489 KB) | HTML iconHTML

    There is an increasing interest in analyzing social interaction from mobile sensor data, and smart phones are rapidly becoming the most attractive sensing option. We propose a new probabilistic relational model to analyze long-term dynamic social networks created by physical proximity of people. Our model can infer different interaction types from the network, revealing the participants of a given... View full abstract»

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  • Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

    Publication Year: 2011, Page(s):29 - 36
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB) | HTML iconHTML

    In this paper, we investigate the relationship between behavioral characteristics derived from rich smart phone data and self-reported personality traits. Our data stems from smart phones of a set of 83 individuals collected over a continuous period of 8 months. From the analysis, we show that aggregated features obtained from smart phone usage data can be indicators of the Big-Five personality tr... View full abstract»

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  • Collaborative PDR Localisation with Mobile Phones

    Publication Year: 2011, Page(s):37 - 40
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (10538 KB) | HTML iconHTML

    We investigate how an ad hoc collaboration between devices which happen to be physically close to each other can improve the quality of pedestrian dead-reckoning (PDR). The general idea is that whenever two users come close to each other, their devices use the proximity information to improve their PDR location estimates. In a public space the improvement will not only affect the two involved user... View full abstract»

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  • South by South-East or Sitting at the Desk: Can Orientation be a Place?

    Publication Year: 2011, Page(s):43 - 46
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2067 KB) | HTML iconHTML

    Location is a key information for context-aware systems. While coarse-grained indoor location estimates may be obtained quite easily (e.g. based on WiFi or GSM), finer-grained estimates typically require additional infrastructure (e.g. ultrasound). This work explores an approach to estimate significant places, e.g., at the fridge, with no additional setup or infrastructure. We use a pocket-based i... View full abstract»

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  • A Context Aware System Based on Scent

    Publication Year: 2011, Page(s):47 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2130 KB) | HTML iconHTML

    Conventional context-aware systems normally use accelerometers and gyroscopes, and it is difficult to recognize contexts such as having a meal, or going to the toilet. We propose a new context recognition method based on scent using a wearable scent sensor. Since our algorithm considers the characteristics of scent, it recognizes contexts that find difficult to recognize by conventional sensors. E... View full abstract»

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  • Sensing Dance Engagement for Collaborative Music Control

    Publication Year: 2011, Page(s):51 - 54
    Cited by:  Papers (2)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (214 KB) | HTML iconHTML

    We introduce a concept that allows attendees of a party to collaboratively influence the music selection process. As explicit feedback is likely to disturb the atmosphere, we introduce an unobtrusive, implicit feedback mechanism. In particular, we propose to sense the partygoers' dance engagement by means of their mobile phones. Since people tend to dance more when they enjoy the music, this metri... View full abstract»

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  • Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices

    Publication Year: 2011, Page(s):55 - 58
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (804 KB) | HTML iconHTML

    Mobile phones or video game controllers using gesture recognition technologies enable easy and intuitive operations. However, usually only one of each type of sensor is installed in each device, and the effect of multiple sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We captured data for 27 kinds... View full abstract»

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  • An Interdisciplinary Undergraduate Design Course for Wearable and Pervasive Computing Products

    Publication Year: 2011, Page(s):61 - 68
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (324 KB) | HTML iconHTML

    This paper reports on a design experience for undergraduates in computer engineering, industrial design, and marketing that focuses on pervasive computing devices. Across a broad range of targeted application areas and user groups, many of the student designs have been wearable computers. Consequently, our course will be of interest to the wearable computing community, particularly in terms of our... View full abstract»

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  • Unsupervised Adaptation to On-body Sensor Displacement in Acceleration-Based Activity Recognition

    Publication Year: 2011, Page(s):71 - 78
    Cited by:  Papers (5)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1534 KB) | HTML iconHTML

    A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacement (e.g. due to replacement or slippage), and lead to changes in the classification performance. We propose an unsupervised adaptive classifier t... View full abstract»

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  • What Can an Arm Holster Worn Smart Phone Do for Activity Recognition?

    Publication Year: 2011, Page(s):79 - 82
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (604 KB) | HTML iconHTML

    While mobile phones are increasingly being used in activity recognition, tasks that require arm motion monitoring have so far not been studied on phone platforms. We leverage the fact that upper arm holsters are an increasingly popular way of wearing mobile devices during physical exercise to investigate the suitability of such platforms for arm dominated activity recognition. We focus on (1) user... View full abstract»

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  • Self-Taught Learning for Activity Spotting in On-body Motion Sensor Data

    Publication Year: 2011, Page(s):83 - 86
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (229 KB) | HTML iconHTML

    Activity spotting has shown to be a highly beneficial approach in context recognition, however lacking robustness limits its widespread use. This work introduces the concept of self-taught learning to activity spotting, which is inspired by natural human learning. The self-taught learning concept was adapted for activity spotting, in particular, to make use of unlabeled data, which does not need t... View full abstract»

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  • Unsupervised Activity Recognition with User's Physical Characteristics Data

    Publication Year: 2011, Page(s):89 - 96
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (356 KB) | HTML iconHTML

    This paper proposes an activity recognition method that models an end user's activities without using any labeled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user's physical characteristics such as height and gender to find other users whose sensor data prepared in advance may be similar to those of the end user. Then, we model the end us... View full abstract»

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