基于多源数据和逻辑推理的行为识别技术研究

doi:10.11896/jsjkx.210300270

Abstract

Abstract: The use of smart terminal equipment to identify and record people's daily behaviors is of great significance for health monitoring,the disabled assistance and elderly care.Most existing related studies adopt machine learning-based ideas,but there are problems such as high consumption of computing resources and training.Due to the heavy burden of data collection and low scalability in different scenarios,this paper proposes a behavior recognition technology based on multi-source perception and logical reasoning.By determining the logical correlation between the actions of different limbs,the accurate description of basic behaviors of users' daily life is realized.Compared with existing work,this technical solution has the advantages of lightweight calculation,low training cost and strong expansion ability to the diversity of users and scenes.This paper realizes a behavior recognition system based on the above technology.A large number of experiments have been carried out to evaluate the performance of the system.The results show that the proposed method has a recognition accuracy of more than 90% for 11 daily behavior activities such as walking,running,and up and down stairs.At the same time,compared with the behavior recognition method based on machine learning,the proposed technique greatly reduces the amount of training data collected by users.

Key words: Activity recognition, Inertial measurement unit, Logical reasoning, Machine learning, Wearable devices

CLC Number:

TP391

XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning[J].Computer Science, 2022, 49(6A): 397-406.

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