移动通信2024，Vol.48Issue(3)：131-136,6.DOI:10.3969/j.issn.1006-1010.20230324-0001
基于IMU传感器与深度度量学习的人体行为识别算法
Human Activity Recognition Algorithm Based on Inertia Measurement Unit Sensors and Deep Metric Learning
摘要
Abstract
Human activity recognition(HAR)is the process of determining a person's various postures and daily activities through a series of observations and the surrounding environment.Many studies have attempted to use deep learning(DL)techniques for HAR.However,existing DL-based HAR methods suffer from issues such as high complexity,large computational requirements,and insufficient generalization and robustness.To address these issues,a new HAR method called RMDML is proposed that focuses on inertia measurement unit(IMU)sensors embedded in smartphones.RMDML combines a lightweight neural network called Residual Multi-Layer Perceptron(Res-MLP)with deep metric learning feature embedding technology to extract generalizable features with separability and discriminability,thereby improving the model recognition performance and generalization ability.RMDML achieves an accuracy of 97.26%on the publicly available UCI HAR dataset,which is higher than several common HAR algorithms,demonstrating the effectiveness of the proposed method.关键词
人体行为识别/惯性测量单元传感器/残差多层感知机/度量学习Key words
human activity recognition(HAR)/inertia measurement unit(IMU)sensor/residual multilayer perceptron(Res-MLP)/metric learning分类
信息技术与安全科学引用本文复制引用
时尚,何正燃,董恒..基于IMU传感器与深度度量学习的人体行为识别算法[J].移动通信,2024,48(3):131-136,6.基金项目
科技部科技创新2030——"新一代人工智能"重大项目(2021ZD0113003) （2021ZD0113003）
