User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

LESSON PLANNING AND TEACHING FINE ART WITH IMPLEMENTATION OF DIGITAL STYLUS

Creativity is closely linked to 21st century competencies. For students in secondary education, it is important to maintain and encourage creativity as part of fine arts classes. This can be achieved through meaningful integration of information and communication technology, as this allows students to express themselves in a more engaging and in-depth way. To help empower class teachers, this research presents a successful example of good practice. Courses are planned in several stages, starting from goals of the subject in relation to digital competencies. The app for the tablet and the digital pen is selected according to the criteria of quality, simplicity of use and relevance. Technology is included in the lessons with the goal to achieve a significant upgrade of the learning experience. Students evaluate their artistic expression as positive and successful. With this activity, students develop their creative and artistic expression abilities to the greatest extent. Their graphic products meet the art task and are aesthetically perfective for their age. The expressive elements within the computer graphics are originally rhythmically arranged. Keywords: digital pen, example of good teaching, fine arts, innovative pedagogy, lesson planning

Advantage of Handwriting Over Typing on Learning Words: Evidence From an N400 Event-Related Potential Index

The growing implementation of digital education comes with an increased need to understand the impact of digital tools on learning. Previous behavioral studies have shown that handwriting on paper is more effective for learning than typing on a keyboard. However, the impact of writing with a digital pen on a tablet remains to be clarified. In the present study, we compared learning by handwriting with an ink pen on paper, handwriting with a digital pen on a tablet, and typing on a keyboard. Behavioral and electroencephalographic indices were measured immediately after learning with each writing tool. The moods of the subjects during the training were also assessed. The participants were divided according to their use of digital pen in their everyday lives, allowing us to take into account the effect of the familiarity with the digital pen on the learning process (familiar group vs. unfamiliar group). We performed an EEG experiment applying a repetition priming paradigm. In each trial, a learned foreign language word (prime word) and a mother tongue word (target word) were consecutively presented. The target word was either semantically identical to the prime word (repetitive condition) or different (non-repetitive condition). We assumed that a larger priming effect on N400 reflects larger learning progress. The familiar group showed a greater N400 priming effect for words learned with the digital or ink pen than those learned with the keyboard. The unfamiliar group showed the greater N400 priming effect for words learned with the ink pen compared with words learned by typing. In addition, positive mood during learning was significantly higher during handwriting than during typing, regardless of the groups. On the other hand, the behavioral indices were not influenced by the writing tool. These results suggest that the movements involved in handwriting allow a greater memorization of new words. The advantage of handwriting over typing might also be caused by a more positive mood during learning. Finally, our results show that handwriting with a digital pen and tablet can increase the ability to learn compared with keyboard typing once the individuals are accustomed to it.

Digital pen technology for conducting cognitive assessments: a cross-over study with older adults

Many digitalized cognitive assessments exist to increase reliability, standardization, and objectivity. Particularly in older adults, the performance of digitized cognitive assessments can lead to poorer test results if they are unfamiliar with the computer, mouse, keyboard, or touch screen. In a cross-over design study, 40 older adults (age M = 74.4 ± 4.1 years) conducted the Trail Making Test A and B with a digital pen (digital pen tests, DPT) and a regular pencil (pencil tests, PT) to identify differences in performance. Furthermore, the tests conducted with a digital pen were analyzed manually (manual results, MR) and electronically (electronic results, ER) by an automized system algorithm to determine the possibilities of digital pen evaluation. ICC(2,k) showed a good level of agreement for TMT A (ICC(2,k) = 0.668) and TMT B (ICC(2,k) = 0.734) between PT and DPT. When comparing MR and ER, ICC(2,k) showed an excellent level of agreement in TMT A (ICC(2,k) = 0.999) and TMT B (ICC(2,k) = 0.994). The frequency of pen lifting correlates significantly with the execution time in TMT A (r = 0.372, p = 0.030) and TMT B (r = 0.567, p < 0.001). A digital pen can be used to perform the Trail Making Test, as it has been shown that there is no difference in the results due to the type of pen used. With a digital pen, the advantages of digitized testing can be used without having to accept the disadvantages.