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Sensors 2016, 16(6), 877;

Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances

Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, N.L. 64849, Mexico
Expanded conference paper based on: Garcia-Ceja, E.; Brena, R. Building Personalized Activity Recognition Models with Scarce Labeled Data Based on Class Similarities. In Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information, Proceedings of the 9th International Conference, UCAmI 2015, Puerto Varas, Chile, 1–4 December 2015; Springer International Publishing: Cham, Switzerland, 2015; Volume 9454, pp. 265–276.
Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo R. García
Received: 27 April 2016 / Revised: 27 May 2016 / Accepted: 31 May 2016 / Published: 14 June 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
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Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce. View Full-Text
Keywords: activity recognition; personalization; accelerometer activity recognition; personalization; accelerometer

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Garcia-Ceja, E.; Brena, R.F. Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances. Sensors 2016, 16, 877.

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