Locally Weighted Ensemble-Detection-Based Adaptive Random Forest Classifier for Sensor-Based Online Activity Recognition for Multiple Residents

• Published in: IEEE internet of things journal Vol. 9; no. 15; pp. 13077 - 13085
• Main Authors: Chen, Dong, Yongchareon, Sira, Lai, Edmund M.-K., Sheng, Quan Z., Liesaputra, Veronica
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-08-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Adaptation models; Classifiers; Concept drift; Data models; Data transmission; Hidden Markov models; Human activity recognition; human activity recognition (HAR); Indoor environments; Intelligent sensors; Machine learning; multiresident activity recognition; online learning (OL); Random forests; Wearable sensors
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2021.3139330

In recent years, various approaches for multiresident human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid development of sensors and AI technologies. Research in data stream-based online learning (OL) for multiresident HAR is relatively new and a majority of the existing works have been developed based on training batches of data that cannot recognize real-time activities. To address the challenges of OL for multiresident HAR, we propose a novel OL architecture based on a locally weighted ensemble detection-based adaptive random forest (LED-ARF) classifier. We conduct a comprehensive performance comparison of eight famous OL classification techniques and our LED-ARF method. The comparison is evaluated based on the two benchmarking CASAS and ARAS data sets. Our experimental results show that LED-ARF achieves the best performance with the highest robustness for online multiresident HAR.