ARAS Human Activity Datasets In Multiple Homes with Multiple Residents

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Date

2013-05-08

Author

Alemdar, Hande
Incel, Ozlem Durmaz
Ersoy, Cem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The real world human activity datasets are of great importance in development of novel machine learning methods for automatic recognition of human activities in smart environments. In this study, we present the details of ARAS (Activity Recognition with Ambient Sensing) human activity recognition datasets that are collected from two real houses with multiple residents during two months. The datasets contain the ground truth labels for 27 different activities. Each house was equipped with 20 binary sensors of different types that communicate wirelessly using the ZigBee protocol. A full month of information which contains the sensor data and the activity labels for both residents was gathered from each house, resulting in a total of two months data. In the paper, particularly, we explain the details of sensor selection, targeted activities, deployment of the sensors and the characteristics of the collected data and provide the results of our preliminary experiments on the datasets.

Subject Keywords

Human activity, Wireless sensor networks

URI

https://hdl.handle.net/11511/36652

DOI

https://doi.org/10.4108/icst.pervasivehealth.2013.252120

Conference Name

7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

H. Alemdar, O. D. Incel, and C. Ersoy, “ARAS Human Activity Datasets In Multiple Homes with Multiple Residents,” presented at the 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Venice, ITALY, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36652.