W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

• Published in: IEEE access Vol. 8; pp. 72870 - 72880
• Main Authors: Wang, Changhai, Wang, Bo, Liang, Hui, Zhang, Jianzhong, Huang, Wanwei, Zhang, Wangwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Algorithms; Classification; Computational modeling; Deep learning; Evaluation; Feature extraction; Hidden Markov Model; Hidden Markov models; Human activity recognition; Machine learning; Markov chains; Moving object recognition; parameter learning; Smoothing methods; Supervised learning; transition matrix; Weights & measures
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2984456

The sensor-based human activity recognition has been wildly applied in behavior tracking, health monitoring, indoor localization etc. Using activity continuity to assist activity recognition is an important research issue, in which the activity transition matrix which describes the activity transformation in real scenarios is the most important parameter. Aiming at the problem that the current classic transition matrix learning algorithm cannot fuse weights of sample classification results, a weighted transition matrix learning algorithm is proposed in this paper. First, the basic definitions of an improved Hidden Markov Model (HMM) which fuses weights of classification results are given. Then, the recursive formula of transition matrix learning is derived, and the learning algorithm W-Trans is put forward. Finally, the proposed algorithm is simulated with the public data sets. The evaluation results show that the proposed algorithm outperforms the classical Baum-Welch algorithm under evaluation metrics of both the cosine similarity and the euler distance. By applying W-Trans to current activity recognition post-process methods, the advantage of our method is verified.