• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于回波导的软超声传感皮肤,用于无需主体参与的手势识别。

EchoGest: Soft Ultrasonic Waveguides Based Sensing Skin for Subject-Independent Hand Gesture Recognition.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2024;32:2366-2375. doi: 10.1109/TNSRE.2024.3414136. Epub 2024 Jul 3.

DOI:10.1109/TNSRE.2024.3414136
PMID:38869995
Abstract

Gesture recognition is crucial for enhancing human-computer interaction and is particularly pivotal in rehabilitation contexts, aiding individuals recovering from physical impairments and significantly improving their mobility and interactive capabilities. However, current wearable hand gesture recognition approaches are often limited in detection performance, wearability, and generalization. We thus introduce EchoGest, a novel hand gesture recognition system based on soft, stretchable, transparent artificial skin with integrated ultrasonic waveguides. Our presented system is the first to use soft ultrasonic waveguides for hand gesture recognition. EcoflexTM 00-31 and EcoflexTM 00-45 Near ClearTM silicone elastomers were employed to fabricate the artificial skin and ultrasonic waveguides, while 0.1 mm diameter silver-plated copper wires connected the transducers in the waveguides to the electrical system. The wires are enclosed within an additional elastomer layer, achieving a sensing skin with a total thickness of around 500 μ m. Ten participants wore the EchoGest system and performed static hand gestures from two gesture sets: 8 daily life gestures and 10 American Sign Language (ASL) digits 0-9. Leave-One-Subject-Out Cross-Validation analysis demonstrated accuracies of 91.13% for daily life gestures and 88.5% for ASL gestures. The EchoGest system has significant potential in rehabilitation, particularly for tracking and evaluating hand mobility, which could substantially reduce the workload of therapists in both clinical and home-based settings. Integrating this technology could revolutionize hand gesture recognition applications, from real-time sign language translation to innovative rehabilitation techniques.

摘要

手势识别对于增强人机交互至关重要,特别是在康复环境中,它可以帮助身体残疾的人恢复,并显著提高他们的活动能力和交互能力。然而,目前的可穿戴手势识别方法在检测性能、可穿戴性和通用性方面往往存在局限性。因此,我们引入了 EchoGest,这是一种基于软、可拉伸、透明的人造皮肤和集成超声波导的新型手势识别系统。我们提出的系统是第一个使用软超声波导进行手势识别的系统。我们使用 EcoflexTM 00-31 和 EcoflexTM 00-45 Near ClearTM 硅酮弹性体来制造人造皮肤和超声波导,而 0.1 毫米直径的镀银铜丝将导波中的换能器连接到电子系统。这些线被包裹在另一个弹性体层中,形成了一个总厚度约为 500 微米的感应皮肤。10 名参与者佩戴 EchoGest 系统,完成了来自两个手势集的静态手势:8 个日常生活手势和 10 个美国手语(ASL)数字 0-9。留一受试者外交叉验证分析表明,日常生活手势的准确率为 91.13%,ASL 手势的准确率为 88.5%。EchoGest 系统在康复方面具有很大的潜力,特别是对手部运动的跟踪和评估,可以大大减少临床和家庭环境中治疗师的工作量。这项技术的集成可能会彻底改变手势识别应用,从实时手语翻译到创新的康复技术。

相似文献

1
EchoGest: Soft Ultrasonic Waveguides Based Sensing Skin for Subject-Independent Hand Gesture Recognition.基于回波导的软超声传感皮肤,用于无需主体参与的手势识别。
IEEE Trans Neural Syst Rehabil Eng. 2024;32:2366-2375. doi: 10.1109/TNSRE.2024.3414136. Epub 2024 Jul 3.
2
Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network.基于 Leap Motion 控制器和两层双向递归神经网络的动态手势识别。
Sensors (Basel). 2020 Apr 8;20(7):2106. doi: 10.3390/s20072106.
3
A Novel Magnetometer Array-based wearable system for ASL gesture recognition.一种基于新型磁力计阵列的可穿戴 ASL 手势识别系统。
Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340708.
4
Hand Gesture Recognition Using Ultrasonic Array with Machine Learning.基于超声阵列和机器学习的手势识别
Sensors (Basel). 2024 Oct 21;24(20):6763. doi: 10.3390/s24206763.
5
On the Benefit of FMG and EMG Sensor Fusion for Gesture Recognition Using Cross-Subject Validation.关于使用跨主体验证的FMG和EMG传感器融合在手势识别中的益处
IEEE Trans Neural Syst Rehabil Eng. 2025;33:935-944. doi: 10.1109/TNSRE.2025.3543649.
6
Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey.用于手势识别的新兴可穿戴接口与算法:一项综述。
IEEE Rev Biomed Eng. 2022;15:85-102. doi: 10.1109/RBME.2021.3078190. Epub 2022 Jan 20.
7
Generalized Cross-Domain Framework for Gesture Recognition via Wrist-Worn Sensing.基于腕部传感的手势识别通用跨域框架
IEEE J Biomed Health Inform. 2025 Feb;29(2):996-1008. doi: 10.1109/JBHI.2024.3496864. Epub 2025 Feb 10.
8
Development of a low-resource wearable continuous gesture-to-speech conversion system.开发一种低资源可穿戴的连续手势到语音转换系统。
Disabil Rehabil Assist Technol. 2023 Nov;18(8):1441-1452. doi: 10.1080/17483107.2021.2022787. Epub 2022 Jan 21.
9
Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove.利用领域变换和深度学习,使用低成本数据手套进行手势识别。
Sci Rep. 2022 Dec 12;12(1):21446. doi: 10.1038/s41598-022-25108-2.
10
A unified framework for gesture recognition and spatiotemporal gesture segmentation.用于手势识别和时空手势分割的统一框架。
IEEE Trans Pattern Anal Mach Intell. 2009 Sep;31(9):1685-99. doi: 10.1109/TPAMI.2008.203.

引用本文的文献

1
Type-2 Neutrosophic Markov Chain Model for Subject-Independent Sign Language Recognition: A New Uncertainty-Aware Soft Sensor Paradigm.用于独立于主体的手语识别的2型中性马尔可夫链模型:一种新的不确定性感知软传感器范式。
Sensors (Basel). 2024 Dec 7;24(23):7828. doi: 10.3390/s24237828.