Machine Learning for Accurate Office Room Occupancy Detection Using Multi-Sensor Data

Abstract

In this paper, we present a comparative study of several machine learning (ML) approaches for accurate office room occupancy detection through the analysis of multi-sensor data. Our study utilizes the occupancy detection dataset, which incorporates data from temperature, humidity, light, and CO2 sensors, with ground-truth labels obtained from time-stamped images captured at minute intervals. Traditional ML techniques, including Decision Trees (DT), Gaussian Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Quadratic Discriminant Analysis (QDA) are compared alongside advanced ensemble methods like RandomForest (RF), Bagging, AdaBoost, GradientBoosting, ExtraTrees as well as our custom voting and multiple stacking classifiers. Also, hyperparameter optimization was performed for selected models with a view to improving classification accuracy. The performances of the models were evaluated through rigorous cross-validation experiments. The results obtained highlight the efficacy and suitability of varying candidate and ensemble methods, demonstrating the potential of ML techniques in enhancing detection accuracy. Notably, LR and SVM exhibited superior performance, achieving average accuracies of 98.88 ± 0.70% and 98.65 ± 0.96%, respectively. Additionally, our custom voting and stacking ensembles demonstrated improvements in classification outcomes compared to base ensemble schemes, as indicated by various evaluation metrics.

Keywords

• machine learning
• ensemble learning
• room occupancy detection
• multi-sensor data