Abstract
Human physical activity monitoring is a relatively new problem drawing much attention over the last years due to its wide application in medicine, homecare systems, prisoner monitoring etc. This paper presents Orthogonal Matching Pursuit as a method for activity recognition and proposes a new modification to the method that significantly increases the recognition accuracy. Both methods show promising results in both total recognition and differentiation between certain activities even without the necessity of prior data preprocessing. The methods were tested on raw sensor data.
This article has been elaborated in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by Operational Programme ’Research and Development for Innovations’ funded by Structural Funds of the European Union and state budget of the Czech Republic and supported by the SGS in VŠB Technical University of Ostrava, Czech Republic, under the grant No. SP2013/70.
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Dohnálek, P., Gajdoš, P., Peterek, T. (2013). Tensor Modification of Orthogonal Matching Pursuit Based Classifier in Human Activity Recognition. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_49
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DOI: https://doi.org/10.1007/978-3-319-00542-3_49
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