In-Air Ultrasonic 3D-Touchscreen with Gesture Recognition Using Existing Hardware for Smart Devices

Dam, Bert Van; Murillo, Yuri; Li, Min; Pollin, Sofie

doi:10.1109/sips.2016.21

Cited by 10 publications

(3 citation statements)

References 5 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The L-shaped microphone array is used to collect samples from the environment where hand movements generate different Doppler shift patterns across time and space. For practical validation, consider that we want to use the array to build a system that detects hand swipes in different directions with a choose gesture (aerial tap), and an extra class to detect an unrecognizable input (−), as described in Definition (9). When a hand performs a gesture, e.g., swipe left, there should be a unique set of temporal ultrasonic patterns (TCN input mono channels) received from the beamformed angles, in the form of the concatenated spectrogram readings shown in Fig.…”

Section: B Tcn Designmentioning

confidence: 99%

Dilate-Invariant Temporal Convolutional Network for Real-Time Edge Applications

Ibrahim¹,

Dool

De³

et al. 2022

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Section: B Tcn Designmentioning

confidence: 99%

Dilate-Invariant Temporal Convolutional Network for Real-Time Edge Applications

Ibrahim¹,

Dool

De³

et al. 2022

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

show abstract

“…An essential part of innovation within the HCI area is the use of new user interfaces, both hardware and software. For example, [6] shows an ultrasonic touchscreen 3D interface with gesture recognition for use on smart devices. This type of interface could be installed in next-generation SMS.…”

Section: State Of the Artmentioning

confidence: 99%

Towards human-computer interaction on smart metering systems

et al. 2020

View full text Add to dashboard Cite

New technologies associated with the fourth industrial revolution are transforming the world in which we live, and the power grid is no exception since it has been provided with intelligence. One of its best-known applications is smart metering systems that allow real-time energy consumption/production to be known, as well as other benefits such as outages and reconnections automatically. The new generations of smart meters have more computing capacity allowing new applications. This work shows some considerations in the design of smart meters using human-computer interaction techniques. The results aim to improve the end-user’s experience and satisfaction and can help to mitigate the reluctance to use smart metering systems in Mexico.;

show abstract

“…The HMM has stronger modelling ability and classification for gesture sequence, especially for gestures with higher complexity than the first three algorithms. [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A study on the algorithm of ultrasonic detection and recognition based on DAG‐SVMs mixed HMM of teleoperation gestures for intelligent manufacturing devices

Liu

Zhang

et al. 2021

IET Collab Intel Manufact

View full text Add to dashboard Cite

Remote control for the position and status of a machine or an equipment can often be teleoperated by gestures in an intelligent manufacturing environment. In order to solve the problems that gestures with two directions such as left and right cannot be detected by single ultrasonic frequency, double different ultrasonic frequencies are used to detect gestures by the Doppler shift, and an algorithm of the recognition gesture based on the DAG‐SVMs mixed Hidden Markov Model (HMM) is proposed to identify and classify the extracted feature sequences. Thus, four more types of gestures are expanded other than that of reading display screen information, and the comparative experiments to classify and recognise gestures of teleoperation are made with DAG‐SVMs, the HMM, the DAG‐SVMs mixed HMM, and other improved HMM algorithms. The test results have shown that the mean rate of gesture recognition for the algorithm based on the DAG‐SVMs mixed HMM is 94.917%, which is 9.497% higher than that of the unimproved HMM, and its recognition accuracy of complex teleoperation gestures is improved by 2.3% compared with other improved HMM algorithms. The experimental results show that the DAG‐SVMs mixed HMM algorithm has a good effect on recognition for the gestures of teleoperation and it can perform gesture recognition accurately.

show abstract

In-Air Ultrasonic 3D-Touchscreen with Gesture Recognition Using Existing Hardware for Smart Devices

Cited by 10 publications

References 5 publications

Dilate-Invariant Temporal Convolutional Network for Real-Time Edge Applications

Dilate-Invariant Temporal Convolutional Network for Real-Time Edge Applications

Towards human-computer interaction on smart metering systems

A study on the algorithm of ultrasonic detection and recognition based on DAG‐SVMs mixed HMM of teleoperation gestures for intelligent manufacturing devices

Contact Info

Product

Resources

About