###### Abstract  
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 [PERSON]'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. user authentication; handwriting analysis; optimal feature; feature selection; machine learning; SVM; F-1 score; sensor data; SFFS +
Footnote †: _Future Internet_**2021**, 13, 231. [[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)). own authorized account which is only accessible by his or her in a protective manner. The authentication methods can be structured into three prime classes: knowledge-based, token-based, and biometric-based. Password-based user authentication is very common but not robust enough. Several researchers investigated user authentication processes based on Keystroke, Hand Geometry, Finger Print, Iris, Face, DNA, Voice, and others. However, every individual's handwriting has distinguishable attributes, which can be used to uniquely authenticate a person both offline or online. In recent years, handwriting recognition and person identification has become a potential field of research. In handwriting recognition system, it takes the input from a touch screen, electronic pen, scanner, images, and paper documents to read, elaborate, and create features of the handwritten characters. Every individual has his or her own individuality in handwriting style. This reality is exercised in various applications like signature verification [1; 2] or person identification [3; 4]. Personal identification based on handwriting is a unique approach that has a variety of potential applications like security purposes, financial activities, forensic activities, demography-based writer authentication, and archaeological authentication (e.g., to identify ancient document writers). Handwriting can also be used to identify the author's identity relating to age, gender, handedness, and ethnicity. This process requires feature extraction from the parameters such as writing speed, pressure, direction, duration, height, width, slant angle, etc.