Sensor Validation for Indoor Air Quality using Machine Learning

Abstract

To guarantee a high indoor air quality is an increasingly important task. Sensors measure pollutants in the air and allow for monitoring and controlling air quality. However, all sensors are susceptible to failures, either permanent or transitory, that can yield incorrect readings. Automatically detecting such faulty readings is therefore crucial to guarantee sensors' reliability. In this paper we evaluate three Machine Learning algorithms applied to the task of classifying a single reading from a sensor as faulty or not, comparing them to standard statistical approaches. We show that all tested machine learning methods -- Multi-layer Perceptron, K-Nearest Neighbor and Random Forest -- outperform their statistical counterparts, both by allowing better separation boundaries and by allowing for the use of contextual information. We further show that this result does not depend on the amount of data, but ML methods are able to continue to improve as more data is made available.

Machine Learning Techniques in Indoor Environmental Quality Assessment

“…Additional studies employed decision trees, hidden Markov models, and stochastic gradient boosting algorithms for immediate and future state occupancy prediction, demonstrating machine learning's potential for real-time IEQ control [57,60]. The advantages of machine learning over traditional statistical approaches were also noted in IAQ sensor reading classification [62].…”

“…These algorithms demonstrated their superiority over standard statistical methods by creating better separation boundaries and utilizing contextual information. Numeric results from a 20-fold cross-validation displayed high average Area Under Curve (AUC) scores for each pollutant: 0.96 for MLP, 0.97 for KNN, and 0.97 for RF, indicating their robust performance in detecting faulty sensor readings [62].…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…In comparison with these linear methods, the nonlinear methods fit more types of data in terms of shape and are hence recognized as being more general. Some nonlinear approaches such as machine learning have the advantage of being less dependent on the assumption of the model and very recently produced promising results in sensor validation [ 25 , 26 ]. Nonlinearity seems particularly interesting in terms of patient monitoring in order to integrate networks of several sensors placed at different places on the patient [ 27 , 28 ] and for high-level tasks (such as the classification of patients into groups according to the evolution of a disease) [ 29 , 30 ], which requires the integration of various information on locomotion and control systems involved in complex gait regulation [ 31 , 32 ].…”