Hand Gesture Signatures Acquisition and Processing by Means of a Novel Ultrasound System

Franceschini, Stefano Sellari; Ambrosanio, Michele; Pascazio, Vito; Baselice, Fabio

doi:10.3390/bioengineering10010036

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…In recent years, more gesture interaction application scenarios have been proposed. Franceschini S et al obtained all hand element information by continuous wave signals without any frequency modulation and used Doppler components to achieve identification in [24]. AcouDigits [25] is proposed as a digital input system to write with fingers in the air to extract different digital echo signals.…”

Section: Acoustic-based Text Inputmentioning

confidence: 99%

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

Yang,

Zhang,

Wang

2023

IET Radar Sonar & Navi

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With the popularity of smart devices, people pay more attention to the experience of text input. The authors propose an in‐air digit input system, namely UltraDigit, which enables users to input digits in the air using acoustic signal via transfer learning. Different from other technologies, UltraDigit can obtain the finger movement through a speaker and a microphone built in the smartphone, which does not need the assistance of other hardware. In addition, the authors use the idea of transfer learning with a small amount of data to train classification model, which achieves good performance with low training cost (such as time cost and data cost). Experimental results show the average accuracy of digit recognition is 92.9%, which verifies the validity of the ultrasound signal‐based contactless digit input.

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Section: Acoustic-based Text Inputmentioning

confidence: 99%

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

Yang,

Zhang,

Wang

2023

IET Radar Sonar & Navi

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“…In order to achieve HGR, Wang et al [26] introduced a novel model named CMFF-HGR, which fuses multi-dimensional features of gesture signals. Franceschini et al [29] proposed an ultrasound system for person identification that exploits hand gestures. The system works as a sonar, measuring the ultrasonic pressure waves scattered by the subject's hand, and analysing its Doppler information.…”

Section: Related Workmentioning

confidence: 99%

Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar

Qiao

Xiao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, Local Binary Pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multilayer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The Gesture-ProxylessNAS module revisits the multi-layer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset [1].

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“…Ref. [16] proposes a novel system for person identification by ultrasonic acquisition of hand posture information, which introduces five ways of processing non-stationary signals: Short-time Fourier Transform (STFT), Wavelet Transform (WT), Stokewell Transform (ST), Hilbert-Huang Transform (HHT) and Constant Q Transform (CQT). In this paper, the Fourier transform is applied to the non-stationary signals acquired by the radar to obtain the range and velocity features of the human body movements, and feature fusion of these two features.…”

Section: Introductionmentioning

confidence: 99%

Human Motion Recognition Based on Feature Fusion and Transfer Learning

Luo,

2024

PIER C

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In order to solve the problem that the recognition accuracy of human motion is not high when a single feature is used, a feature fusion human motion recognition method based on Frequency Modulated Continuous Wave (FMCW) radar is proposed. By preprocessing the FMCW radar echo data, the range and Doppler parameters of human motions are obtained, and the range-time feature map and Doppler-time feature map datasets are constructed. In order to fully extract and accurately identify the human motion features, the two features are fused, and then the two features maps and feature fusion spectrograms are put into the VGG16 network model based on transfer learning for identification and classification. Experimental results show that this method can effectively solve the problem of lack of information and recognition rate of single feature motion recognition, and the recognition accuracy is more than 1% higher than that of the single feature recognition method.

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Hand Gesture Signatures Acquisition and Processing by Means of a Novel Ultrasound System

Cited by 9 publications

References 28 publications

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar

Human Motion Recognition Based on Feature Fusion and Transfer Learning

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