Ahstract-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.
The ARAS data set REF , however, contains the ground truth for 27 different activities in addition to the data from 20 binary sensors.
ARAS human activity datasets in multiple homes with multiple residents
2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops
2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops
Computer Science