Research Article
ARAS Human Activity Datasets in Multiple Homes with Multiple Residents
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2013.252120, author={Hande Alemdar and Ozlem Durmaz Incel and Halil Ertan and Cem Ersoy}, title={ARAS Human Activity Datasets in Multiple Homes with Multiple Residents}, proceedings={7th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2013}, month={5}, keywords={human activity recognition dataset field test wireless sensor networks}, doi={10.4108/icst.pervasivehealth.2013.252120} }- Hande Alemdar
Ozlem Durmaz Incel
Halil Ertan
Cem Ersoy
Year: 2013
ARAS Human Activity Datasets in Multiple Homes with Multiple Residents
PERVASIVEHEALTH
ICST
DOI: 10.4108/icst.pervasivehealth.2013.252120
Abstract
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 Recogni- tion 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.
