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Activity logging using lightweight classification techniques in mobile devices

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Abstract

Automated activity recognition enables a wide variety of applications related to child and elderly care, disease diagnosis and treatment, personal health or sports training, for which it is key to seamlessly determine and log the user’s motion. This work focuses on exploring the use of smartphones to perform activity recognition without interfering in the user’s lifestyle. Thus, we study how to build an activity recognition system to be continuously executed in a mobile device in background mode. The system relies on device’s sensing, processing and storing capabilities to estimate significant movements/postures (walking at different paces—slow, normal, rush, running, sitting, standing). In order to evaluate the combinations of sensors, features and algorithms, an activity dataset of 16 individuals has been gathered. The performance of a set of lightweight classifiers (Naïve Bayes, Decision Table and Decision Tree) working on different sensor data has been fully evaluated and optimized in terms of accuracy, computational cost and memory fingerprint. Results have pointed out that a priori information on the relative position of the mobile device with respect to the user’s body enhances the estimation accuracy. Results show that computational low-cost Decision Tables using the best set of features among mean and variance and considering all the sensors (acceleration, gravity, linear acceleration, magnetometer, gyroscope) may be enough to get an activity estimation accuracy of around 88 % (78 % is the accuracy of the Naïve Bayes algorithm with the same characteristics used as a baseline). To demonstrate its applicability, the activity recognition system has been used to enable a mobile application to promote active lifestyles.

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Notes

  1. From Android developers: http://developer.android.com/guide/developing/debugging/debugging-tracing.html.

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Acknowledgments

Authors acknowledge their lab colleagues for the help provided by the persons who have collaborated on building the activities datasets, and Jesús Cano for his help developing the Activity Monitor. Henar Martín acknowledges the Spanish Ministry of Education for her grant. This work has been supported by the Spanish Ministry for Science and Innovation under grant TIN2011-28620-C02-02 and IPT2011-1052-390000 and the Government of Madrid under grant S2009/TIC-1485 (CONTEXTS).

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  1. Telecommunications School, Universidad Politécnica de Madrid, Av. Complutense 30, 28040, Madrid, Spain

    Henar Martín, Ana M. Bernardos, Josué Iglesias & José R. Casar

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  1. Henar Martín
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Correspondence to Henar Martín.

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Martín, H., Bernardos, A.M., Iglesias, J. et al. Activity logging using lightweight classification techniques in mobile devices. Pers Ubiquit Comput 17, 675–695 (2013). https://doi.org/10.1007/s00779-012-0515-4

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