ARAS Human Activity Datasets in Multiple Homes with Multiple Residents

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 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.

Modeling interaction in multi-resident activities

“…Specifically, we plan to evaluate the proposed models on another data collections like those of ARAS (Alemdar et al, 2013).…”

“…On the contrast, some investigations (Alemdar et al, 2013) (Chen and Tong, 2014) ) did consider interaction between residents. In particular, (Alemdar et al, 2013) (Chen and Tong, 2014) used conventional HMM and CRF showing the advantage of fitting such classical graphical models for single resident activity recognition.…”

“…In particular, (Alemdar et al, 2013) (Chen and Tong, 2014) used conventional HMM and CRF showing the advantage of fitting such classical graphical models for single resident activity recognition. They used respectively a single HMM chain and a single CRF chain to model multi-resident activities by defining "combined labels".…”

“…− We propose a variant of the combined label approach based on HMM applied in (Chen and Tong, 2014) (Alemdar et al, 2013), we call it CL-HMM. In addition to the use of combined labels for the pair of activities labels (i.e., Resident 1 activity label and Resident 2 activity label), our approach also suggests the use of combined labels for the pair of observations (i.e., the observation of Resident 1 and the observation of Resident 2).…”

“…In (Chen and Tong, 2014) both HMM and Conditional Random Field (CRF) were applied and compared. In (Alemdar et al, 2013) HMM was used to model with multi-resident activities.…”