Multiuser Behavior Recognition Module Based on DC-DMN

Jian An

doi:10.1109/JSEN.2021.3133870

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Multiuser Behavior Recognition Module Based on DC-DMN

2022-02-01

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DOI：10.1109/JSEN.2021.3133870

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Abstract

En 中文

The multiuser behavior recognition task based on environmental sensors can provide reliable health monitoring, suspicious person identification and behavior correction. Compared with camera equipment and wearable sensors, the task can achieve acquisition of binary data from the environmental sensors without requiring wearable sensors. Therefore, privacy protection of users and use burden can be improved. However, there are still challenges in this behavior recognition scenario: First, the data consistency shown by the different behaviors of a single user in the same scenario need to be guaranteed. Second, the interactive behavior of multiusers may cause a data association problem. Therefore, the multiuser behavior recognition task based on environmental sensors has, apart from application value, important research challenges. In response, we propose the divide and conquer dynamic memory network model (DC-DMN). Based on the periodicity of user behavior, personal habits, time and spatial characteristics, the multiuser behavior recognition ability of the model can be enhanced. First, the GRU model is used to solve the consistency problem of different behaviors at the data level. Then, we expand the model memory based on the idea of a dynamic memory network. In addition, two sections of memory are designed to integrate and store data more effectively. In this way, the data association and support problem can be solved. Finally, we use three standard datasets to conduct experiments and compare them with the existing benchmark methods in two dimensions of accuracy and recall. Experiments show that DC-DMN performs well in three different datasets. It can effectively solve the problems of data consistency and data association, thereby improving the recognition accuracy.

Keywords:

Sensors

Hidden Markov models

Data models

Task analysis

Wearable sensors

Heuristic algorithms

Intelligent sensors

Multiuser behavior recognition

data association

dynamic memory network framework

attention mechanism