Context Inference for Mobile Applications in the UPCASE Project

Tarrataca, Luís; Cardoso, João M. P.; Ferreira, Diogo R.; Diniz, Pedro C.; Chaínho, Paulo

doi:10.1007/978-3-642-01802-2_26

Cited by 15 publications

(13 citation statements)

References 18 publications

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“…The size of the window depends on the desired resolution for activity monitoring and the available sampling frequency in the device. It should be noted that the window size, usually in order of seconds, is an important parameter that affects both the computation and the power consumption of classification algorithms and must be taken into account when ported to a given portable device [12] [25]. The windowing technique offers a simpler approach to learn the activity models during the training phase over the explicit segmentation approach and it further reduces the computational complexity.…”

Section: Preprocessing and Windowingmentioning

confidence: 99%

Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services

Torres-Huitzil

Alvarez-Landero

2015

Mobile Health

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Abstract. The synergy of communication, computation and sensing capabilities in mobile systems-on-chip artifacts such as smartphones has made possible the development of wearable smart sensor systems for user activity monitoring and recognition. Assessing physical activity is useful to enhance people health as experts have evidenced a clear correlation between physical activity and overweight, obesity and metabolism-related syndromes. Due to user acceptability and the convenient nonintrusive manner for measuring data, smartphones have the advantage of taking proprioceptive motion measurements outside of a controlled environment for rather long periods of time using embedded sensors such as the accelerometer, however, activity recognition poses several challenges. Particularly, though work has been reported for accelerometer-based activity recognition using smartphones, the portability of the device to a single fixed tight position has been a major constraint to easy the interpretation of the collected data on resource-limited devices. In this chapter, a human activity hierarchical recognition system based on neural networks without the need of the smartphone to be constrained to a single fixed position is presented. Yet it is used as a representative example to show the challenges, the role and some of the main potential impacts that smartphones have and will have in mHealth. Experimental results on Android-capable smartphones on four on-body locations show that the recognition system achieves high classification rates, above 92%, for five activities including static, walking, running, and up-down stairs walking, which outperforms other proposals. The system is fully implemented in a smartphone running continuously in near real-time with reduced power consumption in a proof-of-concept client-server application for mHealth.

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Section: Preprocessing and Windowingmentioning

confidence: 99%

Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services

Torres-Huitzil

Alvarez-Landero

2015

Mobile Health

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“…For example, the coefficients from 0.5 Hz to 3 Hz can be used as the key discriminating coefficients for the running and walking activities [47,62].…”

Section: Spectral Analysis Of Key Coefficientsmentioning

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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“…This is due to the increasing offer of added-value services to customers in addition to traditional voice and data communication [102]. Novel added-value services combine features from the Telecom with technologies of the Web; this combination is known as "convergent composition" [23] or "unified composition" [19].…”

Section: What Is Convergent Service Composition?mentioning

confidence: 99%

“…For instance, if the user is currently driving then a service could automatically provide information through voice, such as SIRI [16] and Vlingo [103], instead of text. Another way of taking into account the context of the user is allowing services that can be triggered under specific conditions; for example, it might be possible to determine whether a rescue vehicle is close to an affected area, in which case an emergency notification would be triggered [102].…”

Section: What Is Convergent Service Composition?mentioning

confidence: 99%

Towards automated composition of convergent services: A survey

Ordóñez¹,

Alcázar

Rendón

et al. 2015

Computer Communications

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A convergent service is defined as a service that exploits the convergence of communication networks and at the same time takes advantage of features of the Web. Nowadays, building up a convergent service is not trivial, because although there are significant approaches that aim to automate the service composition at different levels in the Web and Telecom domains, selecting the most appropriate approach for specific case studies is complex due to the big amount of involved information and the lack of technical considerations. Thus, in this paper, we identify the relevant phases for convergent service composition and explore the existing approaches and their associated technologies for automating each phase. For each technology, the maturity and results are analysed, as well as the elements that must be considered prior to their application in real scenarios. Furthermore, we provide research directions related to the convergent service composition phases.

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Context Inference for Mobile Applications in the UPCASE Project

Cited by 15 publications

References 18 publications

Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services

Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services

Preprocessing techniques for context recognition from accelerometer data

Towards automated composition of convergent services: A survey

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