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Temporal features and relations discovery of activities from sensor data

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Abstract

One of the most important problems that arises during the knowledge discovery from data and data mining process in many new emerging technologies is mining data with temporal dependencies. One such application is activity recognition and prediction. Activity recognition is used in many real world settings, such as assisted living systems. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in assisted living settings. We discover temporal relations such as the order of activities, as well as their corresponding start time and duration features. Analysis of real data collected from smart homes was used to validate the proposed method.

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Notes

  1. Results provided in the validation section correspond to the Apt1 dataset, available online at http://eecs.wsu.edu/~nazerfard/AIR/datasets/data1.zip.

  2. Dataset corresponding to Apt2 is also available at http://eecs.wsu.edu/~nazerfard/AIR/datasets/data2.zip.

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Acknowledgements

The author would like to thank D. J. Cook and P. Rashidi for their thorough comments and suggestions on this work.

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  1. Ambient Intelligence Research Laboratory (AIR Lab), Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

    Ehsan Nazerfard

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  1. Ehsan Nazerfard
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Nazerfard, E. Temporal features and relations discovery of activities from sensor data. J Ambient Intell Human Comput 15, 1911–1926 (2024). https://doi.org/10.1007/s12652-018-0855-7

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