A study of touching behavior for authentication in touch screen smart devices

Handwriting-Assistant

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494956 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-25

Author(s):

Yanling Bu ◽

Lei Xie ◽

Yafeng Yin ◽

Chuyu Wang ◽

Jingyi Ning ◽

...

Keyword(s):

Character Recognition ◽

Tracking Error ◽

Recognition Rate ◽

Principal Component ◽

Ground Truth ◽

Touch Screen ◽

Rigid Bodies ◽

Measurement Unit ◽

Smart Devices ◽

Pure Rotation

Pen-based handwriting has become one of the major human-computer interaction methods. Traditional approaches either require writing on the specific supporting device like the touch screen, or limit the way of using the pen to pure rotation or translation. In this paper, we propose Handwriting-Assistant, to capture the free handwriting of ordinary pens on regular planes with mm-level accuracy. By attaching the inertial measurement unit (IMU) to the pen tail, we can infer the handwriting on the notebook, blackboard or other planes. Particularly, we build a generalized writing model to correlate the rotation and translation of IMU with the tip displacement comprehensively, thereby we can infer the tip trace accurately. Further, to display the effective handwriting during the continuous writing process, we leverage the principal component analysis (PCA) based method to detect the candidate writing plane, and then exploit the distance variation of each segment relative to the plane to distinguish on-plane strokes. Moreover, our solution can apply to other rigid bodies, enabling smart devices embedded with IMUs to act as handwriting tools. Experiment results show that our approach can capture the handwriting with high accuracy, e.g., the average tracking error is 1.84mm for letters with the size of about 2cmx1cm, and the average character recognition rate of recovered single letters achieves 98.2% accuracy of the ground-truth recorded by touch screen.

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An Effective Approach to Continuous User Authentication for Touch Screen Smart Devices

2015 IEEE International Conference on Software Quality, Reliability and Security ◽

10.1109/qrs.2015.40 ◽

2015 ◽

Cited By ~ 4

Author(s):

Arun Balaji Buduru ◽

Stephen S. Yau

Keyword(s):

User Authentication ◽

Touch Screen ◽

Smart Devices ◽

Continuous User

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Mutual Capacitive Sensing Touch Screen Controller for Ultrathin Display with Extended Signal Passband Using Negative Capacitance

Sensors ◽

10.3390/s18113637 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3637 ◽

Cited By ~ 3

Author(s):

Chang-Ju Lee ◽

Jong Park ◽

Canxing Piao ◽

Han-Eol Seo ◽

Jaehyuk Choi ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Phase Fluctuation ◽

Touch Screen ◽

Smart Devices ◽

Cmos Process ◽

Negative Capacitance ◽

Driving Frequency ◽

Large Coupling ◽

Quadrature Demodulation ◽

Coupling Capacitance

Flexible and thin displays for smart devices have a large coupling capacitance between the sensor electrode of the touch screen panel (TSP) and the display electrode. This increased coupling capacitance limits the signal passband to less than 100 kHz, resulting in a significant reduction in the received signal, with a driving frequency of several hundred kilohertz used for noise avoidance. To overcome this problem, we reduced the effective capacitance at the analog front-end by connecting a circuit with a negative capacitance in parallel with the coupling capacitance of the TSP. In addition, the in-phase and quadrature demodulation scheme was used to address the phase fluctuation between the signal and the clock during demodulation. We fabricated a test chip using the 0.35 µm CMOS process and obtained a signal-to-noise ratio of 43.2 dB for a 6 mm diameter metal pillar touch input.

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Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Human-centric Computing and Information Sciences ◽

10.1186/s13673-020-00246-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sana Shokat ◽

Rabia Riaz ◽

Sanam Shahla Rizvi ◽

Abdul Majid Abbasi ◽

Adeel Ahmed Abbasi ◽

...

Keyword(s):

Deep Learning ◽

Visually Impaired ◽

False Positive Rate ◽

Touch Screen ◽

Machine Learning Techniques ◽

Smart Devices ◽

Input Method ◽

Visually Impaired People ◽

Learning Techniques ◽

Impaired People

Abstract Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

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