Sound-of-Tapping user interface technology with medium identification

Song, Seok-Jeong; Nam, Hyoungsik

doi:10.1016/j.displa.2018.02.003

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…1. Then, these three sequences are discriminated through a SVM classifier that is a very efficient classifier especially in non-linear problem by comparing the distance with support vectors [17]. Consequently, even when both stylus and finger are placed on the screen, they are recognized separately.…”

Section: Proposed Contact-based Data Communicationmentioning

confidence: 99%

A Contact-Based Data Communication Technique Using Capacitive Touch Screen Panel and Support Vector Machine Classifier

2019

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This paper demonstrates a data communication technology based on the capacitive touch screen scheme that can discriminate stylus and finger using a support vector machine (SVM) classifier. The transmitter in a stylus sends the binary data with no-touch and stylus-touch and the receiver in a touch screen panel (TSP) recovers the received data contents. In addition, the proposed transmission protocol implements an artificial finger-touch on the stylus side for initial and terminal codes. Furthermore, to compensate for common-mode drifts caused by HUM noises, upward and downward drift compensation circuits are proposed for the Tx pulse sensing circuit of the transmitter. The proposed method is evaluated by means of 8-inch capacitive TSP, programmable system-on-chip board, and host processor board. It is verified that the proposed method can support the high data rate of 100 bit per second (bps) or more with less bit error rate than 10 −7 , compared to several bps of previous touch-based communication techniques.

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Section: Proposed Contact-based Data Communicationmentioning

confidence: 99%

A Contact-Based Data Communication Technique Using Capacitive Touch Screen Panel and Support Vector Machine Classifier

2019

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“…Recently, as wearable devices such as smartwatches and smartbands are becoming more popular, small-size displays are becoming further widespread with touch sensing functionality. However, because this very small-area screen cannot support multiple finger-touches and the whole area is covered even by a single finger, a variety of separate input modalities in the outside of the screen have been studied by using infrared (IR) line sensors, microphones, gaze trackers, IR proximity sensors, electric field sensors, deformation sensors, magnetic field sensors, and mechanical interfaces [109][110][111][112][113][114][115][116][117][118][119][120][121][122][123]. In addition, some approaches have coped with the limitation of the single touch by differentiating palm and finger or identifying pad, nail, tip, and knuckle of a finger [124,125].…”

Section: Introductionmentioning

confidence: 99%

Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

Nam

Seol

Lee

et al. 2021

Sensors

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Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. This paper describes the touchscreen technologies in four categories of resistive, capacitive, acoustic wave, and optical methods. Then, it addresses the main studies of SNR improvement and stylus support on the capacitive touchscreens that have been widely adopted in most consumer electronics such as smartphones, tablet PCs, and notebook PCs. In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination.

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“…Groups of data can be classified by finding a subset of data points called support vectors so that the distance between two groups of data points is maximized comparing the distance between these support vectors. 27) Because SVM usually achieve higher performance compared to other classification methods, we used a SVM classifier to discriminate three touch states. No-touch and finger-touch lead to features of different constant value from each other and the stylus-touch makes features of fluctuating values.…”

mentioning

confidence: 99%

Finger and stylus discrimination scheme based on capacitive touch screen panel and support vector machine classifier

Seol

Park

Song

et al. 2019

Jpn. J. Appl. Phys.

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This paper demonstrates a support vector machine (SVM) based capacitive touch screen scheme that can discriminate stylus and finger at the same time. The tip of a stylus provides pulses at the higher frequency than the transmitting (Tx) pulse of a touch screen. Then the digital value acquired from an analog-to-digital converter is transferred to an SVM classifier to make the decision of which touch is applied among no-touch, finger-touch, and stylus-touch. Three types of touches are processed on the SVM algorithm. The proposed method is evaluated by means of an 8 inch capacitive touch panel, connector board, Tx/Rx driver board, and host processor board. While Tx pulses are applied at 5 V and 32 kHz that lead to the 200 Hz reporting rate, stylus pulses are produced at 3 V and 315 kHz. The resultant bit error rate is measured as less than 10−6 for all types of touches.

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Sound-of-Tapping user interface technology with medium identification

Cited by 5 publications

References 9 publications

A Contact-Based Data Communication Technique Using Capacitive Touch Screen Panel and Support Vector Machine Classifier

A Contact-Based Data Communication Technique Using Capacitive Touch Screen Panel and Support Vector Machine Classifier

Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

Finger and stylus discrimination scheme based on capacitive touch screen panel and support vector machine classifier

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