清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Gait-based identification using wearable multimodal sensing and attention neural networks

可穿戴计算机 鉴定(生物学) 计算机科学 步态 人工神经网络 人机交互 人工智能 物理医学与康复 医学 嵌入式系统 生物 植物
作者
Sijia Yi,Zhanyong Mei,Kamen Ivanov,Zijie Mei,Tong He,Hui Zeng
出处
期刊:Sensors and Actuators A-physical [Elsevier BV]
卷期号:374: 115478-115478 被引量:1
标识
DOI:10.1016/j.sna.2024.115478
摘要

Advances in sensor technology have sparked a wave of innovation and progress in the domain of wearable devices. However, ensuring the security of these devices remains a critical concern for their users. This study proposes a novel identification framework that employs custom multimodal sensing insoles and deep learning techniques to contribute to the data security of wearable devices in a user-transparent manner. The insole incorporates an inertial sensing unit and ten force sensors to capture kinematic and kinetic information. For this implementation, we propose a lightweight multiclass classification deep model based on depthwise separable convolution and an enhanced attention mechanism, which operates over multiple signal channels. In designing the recognition model, we focused on two pivotal factors: (1) achieving an optimal balance between computational complexity and recognition accuracy, which is essential for models that operate on devices with limited computational power, and (2) introducing an enhanced attention mechanism that ensures high recognition accuracy without the stringent requirement for precise temporal alignment between sensor channels, which can be technically challenging to attain. We conducted extensive experiments to validate the proposed approach, utilizing a walking dataset collected from 82 young subjects. The results indicate that when using data from the multimodal sensing insole, a high recognition accuracy of 97.96% for person identification is achieved, with a notably low model parameter count of 22,462. The results support the feasibility and effectiveness of sensing footwear-based gait identification. The proposed approach holds significant potential for enhancing user authentication methods across a broad range of scenarios while ensuring transparency and user-friendliness.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
10秒前
科研通AI2S应助科研通管家采纳,获得30
12秒前
Ava应助科研通管家采纳,获得10
12秒前
12秒前
34秒前
elisa828完成签到,获得积分10
38秒前
紫熊发布了新的文献求助10
41秒前
量子星尘发布了新的文献求助10
47秒前
50秒前
1分钟前
lod完成签到,获得积分10
1分钟前
磨刀霍霍阿里嘎多完成签到 ,获得积分10
1分钟前
紫熊发布了新的文献求助10
1分钟前
Liufgui应助水天一色采纳,获得10
1分钟前
fang完成签到,获得积分10
1分钟前
1分钟前
1分钟前
xiaozou55完成签到 ,获得积分10
1分钟前
紫熊发布了新的文献求助20
2分钟前
2分钟前
英俊的铭应助科研通管家采纳,获得10
2分钟前
李健应助科研通管家采纳,获得10
2分钟前
2分钟前
量子星尘发布了新的文献求助10
2分钟前
drhwang完成签到,获得积分10
2分钟前
2分钟前
小强完成签到 ,获得积分10
2分钟前
kangshuai完成签到,获得积分10
2分钟前
水天一色发布了新的文献求助10
2分钟前
3分钟前
Liufgui应助乏味采纳,获得10
3分钟前
3分钟前
bellapp完成签到 ,获得积分10
3分钟前
3分钟前
Liufgui应助Fern采纳,获得30
3分钟前
3分钟前
3分钟前
3分钟前
DSUNNY完成签到 ,获得积分10
3分钟前
4分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
A new approach to the extrapolation of accelerated life test data 1000
Coking simulation aids on-stream time 450
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
Novel Preparation of Chitin Nanocrystals by H2SO4 and H3PO4 Hydrolysis Followed by High-Pressure Water Jet Treatments 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4015340
求助须知:如何正确求助?哪些是违规求助? 3555298
关于积分的说明 11317940
捐赠科研通 3288605
什么是DOI,文献DOI怎么找? 1812284
邀请新用户注册赠送积分活动 887869
科研通“疑难数据库(出版商)”最低求助积分说明 811983