Abstract
Usually, approaches driven by data proposed in literature for sensor-based activity recognition use the begin label and the end label of each activity in the dataset, fixing a temporal window with sensor data events to identify the activity carried out in this window. This type of approach cannot be carried out in real time because it is not possible to predict the start time of an activity, i.e., the class of the future activity that an inhabitant will perform, neither when he/she will begin to carry out this activity. However, an activity can be marked as finished in real time only with the previous observations. Therefore, there is a need of online activity recognition approaches that classify activities using only the end label of the activity. In this paper, we propose and evaluate a new approach for online activity recognition with three temporal sub-windows that uses only the end label of the activity. The advantage of our approach is that the temporal sub-windows keep a partial order in the sensor data stream from the end time of the activity in a short-term, medium-term, long-term. The experiments conducted to evaluate our approach suggest the importance of the use of temporal sub-windows versus a single temporal window in terms of accuracy, using only the end time of the activity. The use of temporal sub-windows has improved the accuracy in the 98.95% of experiments carried out.
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https://ailab.wsu.edu/casas/datasets (last checked on February 28, 2018)
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No 734355 together the Spanish government by research project TIN2015-66524-P.
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Espinilla, M., Medina, J., Hallberg, J. et al. A new approach based on temporal sub-windows for online sensor-based activity recognition. J Ambient Intell Human Comput 14, 15957–15969 (2023). https://doi.org/10.1007/s12652-018-0746-y
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DOI: https://doi.org/10.1007/s12652-018-0746-y