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Open AccessArticle

Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries

MAmI Research Lab, University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 Ciudad Real, Spain
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This paper is an extended version of our paper published in Gutiérrez López de la Franca, C.; Hervás, R.; Johnson, E.; Bravo, J. Findings about Selecting Body Parts to Analyze Human Activities through Skeletal Tracking Joint Oriented Devices. In Proceedings of the 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAMI 2016), Gran Canaria, Spain, 29 November–2 December 2016.
Sensors 2018, 18(5), 1665; https://doi.org/10.3390/s18051665
Received: 4 April 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 22 May 2018
This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better. View Full-Text
Keywords: activity recognition; Kinect; ubiquitous computing; ambient intelligence; extended body-angles algorithm activity recognition; Kinect; ubiquitous computing; ambient intelligence; extended body-angles algorithm
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Gutiérrez-López-Franca, C.; Hervás, R.; Johnson, E. Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries. Sensors 2018, 18, 1665.

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