Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.

IF 3.8 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Guoming Du, Zhen Ding, Hao Guo, Meichao Song, Feng Jiang
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引用次数: 0

Abstract

Estimating human joint angles is a crucial task in motion analysis, gesture recognition, and motion intention prediction. This paper presents a novel model-based approach for generating reliable and accurate human joint angle estimation using a dual-branch network. The proposed network leverages combined features derived from encoded sEMG signals and RGB-D image data. To ensure the accuracy and reliability of the estimation algorithm, the proposed network employs a convolutional autoencoder to generate a high-level compression of sEMG features aimed at motion prediction. Considering the variability in the distribution of sEMG signals, the proposed network introduces a vision-based joint regression network to maintain the stability of combined features. Taking into account latency, occlusion, and shading issues with vision data acquisition, the feature fusion network utilizes high-frequency sEMG features as weights for specific features extracted from image data. The proposed method achieves effective human body joint angle estimation for motion analysis and motion intention prediction by mitigating the effects of non-stationary sEMG signals.

利用 sEMG 信号和 RGB-D 摄像机估算下肢关节角度
估计人体关节角度是运动分析、手势识别和运动意图预测中的一项重要任务。本文提出了一种基于模型的新方法,利用双分支网络生成可靠、准确的人体关节角度估计。所提出的网络利用了从编码 sEMG 信号和 RGB-D 图像数据中获得的组合特征。为确保估算算法的准确性和可靠性,所提出的网络采用了卷积自动编码器,对旨在进行运动预测的 sEMG 特征进行高级压缩。考虑到 sEMG 信号分布的可变性,拟议网络引入了基于视觉的联合回归网络,以保持组合特征的稳定性。考虑到视觉数据采集的延迟、遮挡和阴影问题,特征融合网络利用高频 sEMG 特征作为从图像数据中提取的特定特征的权重。所提出的方法通过减轻非稳态 sEMG 信号的影响,实现了用于运动分析和运动意图预测的有效人体关节角度估计。
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来源期刊
Bioengineering
Bioengineering Chemical Engineering-Bioengineering
CiteScore
4.00
自引率
8.70%
发文量
661
期刊介绍: Aims Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal: ● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings. ● Manuscripts regarding research proposals and research ideas will be particularly welcomed. ● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. ● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds. Scope ● Bionics and biological cybernetics: implantology; bio–abio interfaces ● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices ● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc. ● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology ● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering ● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation ● Translational bioengineering
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