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.

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.

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.

This paper proposes an anomaly detection (AD) algorithm that can discriminate stylus-touch based on capacitive touch screen panel. The digital value acquired from an analog-to-digital converter (ADC) are transferred to an autoencoder including an encoder and a decoder. While the encoder classifies only two classes of a no-touch and a finger-touch, the decoder reconstructs the similar sequence to the input one according to the encoder's decision. Because the touch sequences caused by the stylus are not trained, the large difference between input and output sequences is used to discriminate the stylus-touch from finger-touch and no-touch. The proposed method is evaluated by means of an 8-inch capacitive touch panel, an AD touch detection board, and a stylus board. At the sequence length of 16 touch samples, the measured bit error rate (BER) of less than 10 −6 for each touch case is equivalent to the previous support vector machine (SVM) scheme whereas the number of multipliers is dramatically reduced to 16, compared with 400 of the previous SVM method.

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