Abstract
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.
Highlights
This section first introduces a common framework of activity recognition, and point out the position of transition matrix learning in this framework
The sensor-based human activity recognition has been wildly applied in behavior tracking, health monitoring, indoor localization etc
As the most import parameter, the transition matrix should be learned from the classification results sequence dynamically
Summary
C. Wang et al.: W-Trans: Weighted Transition Matrix Learning Algorithm for the Sensor parameter is the basis for calculating the confidence of recognition results and smoothing the activity results sequence. VOLUME 8, 2020 some applications such as the Google activity recognition service just provide the activity results but not the raw sensor data [20] In these scenes, the deep learning models cannot take advantage of them. An accurate transition matrix can provide effective results on applying activity sequences to activity recognition This parameter can be trained with the classical Baum-Welch algorithm [16], [17] which is a basic algorithm in HMM theory. The classical Baum-Welch algorithm learns the transition matrix with the recognized label sequence L, and our proposed W-Trans will learn this parameter with the normalized result sequence O. The following subsections will give the basic definitions and detail our algorithm
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