Transformer With Bidirectional GRU for Nonintrusive, Sensor-Based Activity Recognition in a Multiresident Environment

• Published in: IEEE internet of things journal Vol. 9; no. 23; pp. 23716 - 23727
• Main Authors: Chen, Dong, Yongchareon, Sira, Lai, Edmund M.-K., Yu, Jian, Sheng, Quan Z., Li, Yafeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-12-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Adaptive systems; Bidirectional GRU (Bi-GRU); Collaboration; Complexity; Deep learning; Hidden Markov models; Human activity recognition; human activity recognition (HAR); Indoor environments; Machine learning; Motion detection; multiresident activity recognition; Smart homes; Support vector machines; transformer architecture; Transformers
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3190307

Several techniques for human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid advancement of sensor technologies. However, recognizing multiple people's activities is still challenging due to the complexity of their activities, such as parallel and collaborative activities. To address these challenges, we propose a transformer with a bidirectional gated recurrent unit (GRU) deep learning (DL) method, called TRANS-BiGRU, to efficiently learn and recognize different types of activities performed by multiple residents. We compare the proposed model with the state-of-the-art models and various DL models, such as Ensemble2LSTM (Ens2-LSTM), bidirectional GRUs (Bi-GRU), and traditional machine learning (ML) models, such as support vector machine (SVM). Our experimental results based on the center for advanced studies in adaptive system and ARAS public data sets show that our model significantly outperforms the existing models for complex activity recognition of multiple residents.