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Preprocessing techniques for context recognition from accelerometer data

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Abstract

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 whether 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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Notes

  1. For example, the Nike+ project (http://www.nikerunning.nike.com/nikeplus/) collects data captured by an accelerometer located on the user’s running shoes. The user can then upload the data to a personal computer and use an application that analyzes the running habits and physical effort to recommend training regimes.

  2. Characteristics such as clock rates, caches, functional units and pipelines could influence the results of such comparison, as some techniques may be more amenable to specific architectural or compiler features.

  3. In the simplest Wavelet examined, the Haar wavelet of order 2 (H 2 = [11; 1 − 1]) only additions and subtractions are used and divisions are always by constant, which is optimized in many Floating-Point Units (FPU) hardware designs.

  4. Although clever implementations can reduce this requirement to a linear relationship.

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Authors and Affiliations

  1. IST, Technical University of Lisbon, Avenida Prof. Dr. Cavaco Silva, 2744-016, Porto Salvo, Portugal

    Davide Figo, Pedro C. Diniz & Diogo R. Ferreira

  2. Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João M. P. Cardoso

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  1. Davide Figo
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  2. Pedro C. Diniz
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  3. Diogo R. Ferreira
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  4. João M. P. Cardoso
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Correspondence to Diogo R. Ferreira.

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Figo, D., Diniz, P.C., Ferreira, D.R. et al. Preprocessing techniques for context recognition from accelerometer data. Pers Ubiquit Comput 14, 645–662 (2010). https://doi.org/10.1007/s00779-010-0293-9

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