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一种基于多通道生物阻抗的越南手语识别设备。

A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition.

作者信息

Than Nhat-Minh, Nguyen Son-Thuy, Huynh Dang-Nguyen, Tran Thao-Nguyen, Le Nguyen-Khoa, Mai Huu-Xuan, Le Cao-Dang, Pham Tan-Thi, Huynh Quang-Linh, Nguyen Trung-Hau

机构信息

Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, 700000, Vietnam.

Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, 700000, Vietnam.

出版信息

Sci Rep. 2024 Dec 30;14(1):31830. doi: 10.1038/s41598-024-83108-w.

DOI:10.1038/s41598-024-83108-w
PMID:39738434
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11686314/
Abstract

This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need.

摘要

本研究探讨了手势识别在工业、教育和医疗等多个领域日益增长的重要性，针对聋哑群体常常被忽视的需求。主要目标是改善个体之间的沟通，从而提高整体生活质量，特别是在先进医疗保健的背景下。本文提出了一种使用生物阻抗技术进行实时手势识别的新方法。所开发的设备由树莓派供电，并连接到电极以采集阻抗数据，采用阻抗芯片进行数据收集。为了对手势进行分类，使用了卷积神经网络（CNN）、XGBoost和随机森林。该模型成功识别了多达九种不同的手势，采用依赖于受试者的策略，在十名受试者中平均准确率达到97.24%，展示了基于生物阻抗的系统在手势识别中的有效性。这些有前景的结果为未来人机之间非语言通信的发展奠定了基础，因为它有助于推动技术进步，造福听力受损者，满足一项关键的社会需求。

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SN Comput Sci. 2021;2(6):436. doi: 10.1007/s42979-021-00827-x. Epub 2021 Aug 29.
3
ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition.查尔林恩：人眼追踪：IsoGD 和 ConGD 大规模 RGB-D 手势识别。
IEEE Trans Cybern. 2022 May;52(5):3422-3433. doi: 10.1109/TCYB.2020.3012092. Epub 2022 May 19.
4
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.基于注意力的新型混合 CNN-RNN 结构用于基于 sEMG 的手势识别。
PLoS One. 2018 Oct 30;13(10):e0206049. doi: 10.1371/journal.pone.0206049. eCollection 2018.
5
A Handheld and Textile-Enabled Bioimpedance System for Ubiquitous Body Composition Analysis. An Initial Functional Validation.一种用于无处不在的身体成分分析的手持式且具备纺织品功能的生物阻抗系统。初步功能验证。
IEEE J Biomed Health Inform. 2017 Sep;21(5):1224-1232. doi: 10.1109/JBHI.2016.2628766. Epub 2016 Nov 15.
6
Portable bioimpedance monitor evaluation for continuous impedance measurements. Towards wearable plethysmography applications.用于连续阻抗测量的便携式生物阻抗监测仪评估。面向可穿戴体积描记法应用。
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:559-62. doi: 10.1109/EMBC.2013.6609561.
7
Assessment of alterations in the electrical impedance of muscle after experimental nerve injury via finite-element analysis.通过有限元分析评估实验性神经损伤后肌肉的电阻抗变化。
IEEE Trans Biomed Eng. 2011 Jun;58(6):1585-91. doi: 10.1109/TBME.2011.2104957. Epub 2011 Jan 10.
8
Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses.基于模式识别的多功能经桡动脉假肢肌电控制量化研究。
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9
Decoding of individuated finger movements using surface electromyography.使用表面肌电图对个体化手指运动进行解码。
IEEE Trans Biomed Eng. 2009 May;56(5):1427-34. doi: 10.1109/TBME.2008.2005485.
10
Estimation of body water compartments in cirrhosis by multiple-frequency bioelectrical-impedance analysis.通过多频生物电阻抗分析评估肝硬化患者的体水分布情况。
Nutrition. 2001 Jan;17(1):31-4. doi: 10.1016/s0899-9007(00)00473-1.