Abstract—Analyzing human behavior in smart
environments is an important research area dealing with a
multitude of issues related to ubiquitous computing, machine
learning and ambient assisted living. With recent
advancements in sensing technologies, it is henceforth possible
to build computational models that select relevant sensors, and
apply statistical models for accurate detection of residents’
activities in smarthomes. To this end, we choose to work with
the “Orange4home” dataset which represents one of the latest
dataset in this research field. The main contribution of this
paper is (1) to perform accurate detection of resident activities
(extracted from the “Orange4Home” dataset) by proposing
relevant preprocessing and machine learning approaches, and
(2) enhance previous classification results already published on
the same dataset. Thus, our methodology in this paper is to
explore the whole process from data preprocessing to
classification metrics. Indeed, a carefully designed and original
preprocessing algorithm is proposed in order to properly
prepare data to the training phase. Then, to perform relevant
exploration of the feature space, many strategies for features
selection and reduction (based on Univariate feature selection
and Principal Component Analysis) were proposed. For the
activities classification task, many well-chosen discriminative
models (SVMs, Decision Trees, Random Forests) were
explored. Our main results outperform previously published
results on the same dataset. Moreover, comparing all proposed
classifiers, Random Forests outperform other classifiers and
shows that the optimal accuracy rate (95%) was obtained
thanks to a smart choice of a limited number of sensors rather
the use of the full feature space (i.e. data from all installed
sensors). Based on our results, many recommendations (for
building optimal smarthomes and activity classification models)
were emphasized in the end of this paper.
Index Terms—Activity classification, data preprocessing
and feature selection, discriminative models, smarthome.
The authors are with the Department of Management Information Systems,
College of Business and Economics, Qassim University, P.O. Box: 6640,
Buraidah: 51452, Saudi Arabia (e-mail: a.mihoub@qu.edu.sa,
s.zidi@qu.edu.sa, l.laouamer@qu.edu.sa).
Cite: A. Mihoub, S. Zidi, and L. Laouamer, "Investigating Best Approaches for Activity Classification in a Fully Instrumented Smarthome Environment," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 299-308, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).