Abstract
The uncertainty associated with existing sensing technologies and reasoning methods affects the outcome of the activity recognition process (e.g., accuracy, precision, granularity). The activity recognition process is even challenging when switching from laboratory towards real deployments, where scenarios are not predefined and more complex. Therefore we propose a novel method to improve the activity recognition outcome, by finding a proper balance between accuracy and granularity. The method has been validated through the deployment of UbiSMART (an AAL framework) in 45 scenarios of ageing in place. We discuss in this paper our method and the validation results.
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Acknowledgments
This research project has been supported by the Quality Of Life Chair supported by Foundation Telecom of the Institut Mines-Telecom in France, La Mutuelle Generale and REUNICA which figure among the major health-care insurance companies in France. The work is also supported by the grand emprunt VHP inter@ctive project. We also wish to acknowledge the support of the Saint-Vincent-de-Paul nursing home and its director Brigitte Choquet, who kindly let us deploy our system within their environment.
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Aloulou, H., Endelin, R., Mokhtari, M., Abdulrazak, B., Kaddachi, F., Bellmunt, J. (2017). Activity Recognition Enhancement Based on Ground-Truth: Introducing a New Method Including Accuracy and Granularity Metrics. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Enhanced Quality of Life and Smart Living. ICOST 2017. Lecture Notes in Computer Science(), vol 10461. Springer, Cham. https://doi.org/10.1007/978-3-319-66188-9_8
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DOI: https://doi.org/10.1007/978-3-319-66188-9_8
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