Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications

Wang, Tiantong; Zhao, Yunbiao; Wang, Qining

doi:10.1109/tnsre.2022.3212705

Cited by 13 publications

(2 citation statements)

References 30 publications

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“…Employing a triplet network, Tiantong Wang [10] suggested using a wristband with a very responsive multichannel capacitance array for hand motion identification across days. When the capacitive array's pixelated outputs were interpreted as picture frames, the stress patterns all around wrist have been converted into visible patterns.…”

Section: Related Workmentioning

confidence: 99%

An in-Depth Survey on Online Shopping Through Gesture Recognition Using Deep Learning

2023

IRJMETS

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The recent advances in communication capabilities and the proliferation of electronic devices has been effective in improving the online landscape by a large margin. There has been introduction of innovative utilizations of the internet platform in the field of shopping has led to the development and success of ecommerce websites. These websites allow an individual to make purchases from the comfort of their homes. This is incredibly useful and highly convenient practice that makes shopping an easier and highly rewarding experience online. But the problem with these applications is the lack of an effective accessibility features that allow for the disabled people to do shopping. Therefore, there is a need for an approach that can provide the effective interaction and navigation of the e-commerce websites through the use of gestures. For this purpose the computer vision approach is being utilized to capture the gesture and allow for navigating the shopping application through the use of gestures. The presented approach achieves effective gesture based shopping through the implementation of Convolutional Neural networks. This methodology will be elaborated in the upcoming editions of this research article.

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Section: Related Workmentioning

confidence: 99%

An in-Depth Survey on Online Shopping Through Gesture Recognition Using Deep Learning

2023

IRJMETS

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“…H AND gesture recognition (HGR) has become a vital role in an intuitive and practical human-machine interface (HMI). An increasing number of studies are focusing on pursuing the high effectiveness and quite robustness of HGR-based HMI, because these directly affect the acceptance of users and the efficiency of collaboration with external machines [1]- [5]. The HGR based on surface electromyogram (sEMG) and Accelerometer (ACC) signal has expressed enormous potential [6]- [9] for two reasons.…”

Section: Introductionmentioning

confidence: 99%

Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition

Duan,

Wu,

Liu

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.

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A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Cao,

Ma,

Chen

et al. 2024

Advanced Intelligent Systems

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Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

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Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications

Cited by 13 publications

References 30 publications

An in-Depth Survey on Online Shopping Through Gesture Recognition Using Deep Learning

An in-Depth Survey on Online Shopping Through Gesture Recognition Using Deep Learning

Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

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