Abstract
The analysis of daily living human behavior has proven to be of key importance to prevent unhealthy habits. The diversity of activities and the individuals’ particular execution style determine that several sources of information are normally required. One of the main issues is to optimally combine them to guarantee performance, scalability and robustness. In this paper we present a fusion classification methodology which takes into account the potential of the individual decisions yielded at both activity and sensor classification levels. Particularly tested on a wearable sensors based system, the method reinforces the idea that some parts of the body (i.e., sensors) may be specially informative for the recognition of each particular activity, thus supporting the ranking of the decisions provided by each associated sensor decision entity. Our method systematically outperforms the results obtained by traditional multiclass models which otherwise may require a high-dimensional feature space to acquire a similar performance. The comparison with other activity-recognition fusion approaches also demonstrates our model scales significantly better for small sensor networks.
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Notes
Other approaches as the one-versus-one may be similarly applied, but here, the one-versus-rest is particularly recommended to reduce the number of classification entities.
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Acknowledgments
We want to express our gratitude to Prof. Stephen S. Intille, Technology Director of the HouseN Consortium in the MIT Department of Architecture for the experimental data provided. This work was supported in part by the Spanish CICYT Project TIN2007-60587, Junta de Andalucia Projects P07-TIC-02768 and P07-TIC-02906, the CENIT project AmIVital, of the “Centro para el Desarrollo Tecnolgico Industrial” (CDTI-Spain), the FPU Spanish Grant AP2009-2244 and the UGR Spanish Grant “Iniciación a la Investigación 2010/2011”.
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Banos, O., Damas, M., Pomares, H. et al. Human activity recognition based on a sensor weighting hierarchical classifier. Soft Comput 17, 333–343 (2013). https://doi.org/10.1007/s00500-012-0896-3
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DOI: https://doi.org/10.1007/s00500-012-0896-3