SenSay: a context-aware mobile phone

Siewiorek, Dan; Smailagic, Asim; Furukawa, J.; Krause, Andreas; Moraveji, Neema; Reiger, K.; Shaffer, Jeremy; Wong, Fei Lung

doi:10.1109/iswc.2003.1241422

Cited by 201 publications

(134 citation statements)

References 7 publications

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“…We collected a variety of features based on sensor and other data that we can actively collect and have been shown to be effective at determining mobile interruptibility (e.g., [7,25,27]) ( Table 1). Examples of these features include GPS longitude, latitude, the time of day, and whether the user is talking on the phone.…”

Section: Phone Interruption Featuresmentioning

confidence: 99%

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Rosenthal

Dey

Veloso

2011

Lecture Notes in Computer Science

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Abstract. We contribute a method for approximating users' interruptibility costs to use for experience sampling and validate the method in an application that learns when to automatically turn off and on the phone volume to avoid embarrassing phone interruptions. We demonstrate that users have varying costs associated with interruptions which indicates the need for personalized cost approximations. We compare different experience sampling techniques to learn users' volume preferences and show those that ask when our cost approximation is low reduce the number of embarrassing interruptions and result in more accurate volume classifiers when deployed for long-term use.

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Section: Phone Interruption Featuresmentioning

confidence: 99%

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Rosenthal

Dey

Veloso

2011

Lecture Notes in Computer Science

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“…This approach has been used in [55] to make a crude detection between classes of activities by inspecting of the maximum values (or peaks) of the differences between all axes to determine the type of movement.…”

Section: Sample Differencesmentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

Figo

Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

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The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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“…Other studies on semantic place labeling so far [Reddy et al, 2010, Consolvo et al, 2008, Arase et al, 2010, Bouten et al, 1997, Perrin et al, 2000, Junker et al, 2004, Preece et al, 2009, Berchtold et al, 2010, Ravi et al, 2005, Bao and Intille, 2004, Chang et al, 2007, Farringdon et al, 1999, Kern et al, 2003, Mantyjarvi et al, 2001, Stikic et al, 2008, Zinnen et al, 2009, Lester et al, 2005, Siewiorek et al, 2003 are mostly based on unlabeled data or on a small number of sensor and state data. The field of physical activity recognition based on accelerometer sensor data is well researched [Consolvo et al, 2008, Arase et al, 2010, Berchtold et al, 2010, Bao and Intille, 2004, Farringdon et al, 1999, Kern et al, 2003].…”

Section: Related Workmentioning

confidence: 99%