[1]衣淳植,贾翊丞,姜峰,等.基于惯性测量单元的人体运动意图识别方法：现状与挑战[J].智能系统学报,2025,20(4):763-775.[doi:10.11992/tis.202407012]
　YI Chunzhi,JIA Yicheng,JIANG Feng,et al.Human motion intention recognition method based on inertial measurement unit: current situation, and challenges[J].CAAI Transactions on Intelligent Systems,2025,20(4):763-775.[doi:10.11992/tis.202407012]
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基于惯性测量单元的人体运动意图识别方法：现状与挑战

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 20 期数: 2025年第4期 页码: 763-775 栏目: 综述 出版日期: 2025-08-05
Title:
Human motion intention recognition method based on inertial measurement unit: current situation, and challenges
作者:
衣淳植1, 贾翊丞1, 姜峰2, 王修来3
1. 哈尔滨工业大学 生命科学与医学学部, 黑龙江 哈尔滨 150001;
2. 哈尔滨工业大学 计算学部, 黑龙江 哈尔滨 150001;
3. 东部战区总医院, 江苏 南京 210018
Author(s):
YI Chunzhi1, JIA Yicheng1, JIANG Feng2, WANG Xiulai3
1. Department of Life Science and Medicine, Harbin Institute of Technology, Harbin 150001, China;
2. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China;
3. General Hospital of Eastern Theater, Nanjing 210018, China
关键词:
人体行为识别; 深度学习; 惯性传感器; 普适计算; 数据隐私; 模型部署; 迁移学习; 数据质量
Keywords:
human activity recognition; deep learning; inertial sensors; ubiquitous computing; data privacy; model deployment; transfer learning; data quality
分类号:
TP181; Q81
DOI:
10.11992/tis.202407012
文献标志码:
2025-2-25
摘要:
人体行为识别(human activity recognition, HAR)利用可穿戴计算、机器学习等技术识别和理解人体行为，在行为跟踪、健康监测及人机交互等领域得到广泛应用，极大提升了当下人类的生活水平。当前可穿戴传感器中，惯性传感器由于其高度小型化、低成本、信号稳定等优势，已经日益成为可穿戴计算领域的主流应用设备。基于此，HAR领域内较多的研究以惯性信号作为数据源，并通过应用深度学习算法，以应对在数据利用率、隐私保护、模型部署等方面的挑战。本文系统地介绍面向HAR的深度学习方法并对现有工作进行了分类和总结，对于当前进展、发展趋势和主要挑战进行了全面分析。首先，本文介绍当前用于HAR研究的主流可穿戴设备及其数据模态，并对各模态数据特点进行介绍。其次，整理近年来常用的HAR数据集，并对各数据集中包含的数据模态、传感器位置、运动种类以及被引用次数等进行汇总。再次，本文从算法特点、应用场景等方面总结了当前HAR领域主要应用的几种深度学习方法的进展。最终，讨论当前HAR领域深度学习面临的挑战与潜在解决方法。
Abstract:
Human activity recognition (HAR) utilizes wearable computing, machine learning, and other technologies to identify and understand human behaviors, which remarkably enhances current human living standards in areas such as behavior tracking, health monitoring, and human–computer interaction. Inertial sensors have increasingly become the mainstream devices in wearable computing due to their highly compact size, low cost, and stable signal characteristics. Consequently, much research in the HAR field employs inertial signals as data sources and applies deep learning algorithms to address challenges in data utilization, privacy protection, and model deployment. This paper systematically introduces deep learning approaches for HAR and categorizes and summarizes existing work, and comprehensively analyzes current advancements, development trends, and key challenges. First, this paper introduces mainstream wearable devices used in HAR research and their data modalities, and details the characteristics of each modality. Second, this paper compiles commonly used HAR datasets in recent years and summarizes the data modalities, sensor placements, movement types, and citation frequencies within each dataset. Furthermore, the paper reviews the progress of several deep learning methods commonly applied in the HAR field from the perspectives of algorithm characteristics and application scenarios. Finally, this paper discusses the challenges currently confronting deep learning in the HAR field and the potential solutions.
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备注/Memo

收稿日期:2024-7-10。
基金项目:江苏省科技计划项目(BE2021086); 中央引导地方科技发展专项(2024ZYD0266).
作者简介:衣淳植，副教授，博士，主要研究方向为神经信号处理，运动-认知交互，人体生物力学，柔性外骨骼机器人。E-mail：chunzhiyi@hit.edu.cn。;贾翊丞，科研助理，主要研究方向为生物信号处理、可穿戴计算与人体意图预测。E-mail：3125763352@qq.com。;王修来,教授,博士生导师，博士,南京信息工程大学人才大数据研究院院长，教育部“泛在网络与健康服务系统”工程研究中心副主任，江苏省“333高层次人才培养工程”第二层次培养对象，江苏省有突出贡献的中青年专家，国务院政府特殊津贴获得者，《技术经济与管理研究》杂志副主编，中国继续教育学会理事，江苏省人才创新创业促进会理事。主要研究方向为人力资源管理与信息不对称、大数据挖掘与分析和数据智能应用。作为主持人、负责人或主要完成人，先后完成国家级、省部级等科研项目和立项课题45项，获省部级优秀科研成果一等奖2项、二等奖2项，科技进步二等奖5项、三等奖4项；申请、获得国家专利授权共23项。发表学术论文132篇，出版著作17本，其中专著3本、主编9本、编著5本。E-mail：wangxiulai@126.com。
通讯作者:王修来. E-mail：wangxiulai@126.com

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