###### Abstract  
Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base.  
touchscreen handwriting; biometrics; sensor fusion; apparatus; authentication Article  
A Sensor-Fusion-Based Experimental Apparatus for Collecting Touchscreen Handwriting Biometric Features  
To enhance security measures and facilitate the verification of individuals, a range of biometric identifiers have been devised. These factors include fingerprint patterns, facial features, vocal characteristics, walking patterns, iris structure, signatures, etc.  
Considering the importance of signatures as a biometric factor in user identification, numerous papers have been devoted to this subject. This discipline comprises traditional and digital forms of handwriting. Depending on the signature acquisition method, two types of signature verification techniques exist, referring to handwritten text being produced offline and online.  
Offline (static) verification pertains to the analysis of handwriting after the procedure of creating a signature has been concluded. The methodology entails the scanning or capturing of handwritten papers, in which a digital picture serves as a representation of the signature [4]. The offline approach is particularly relevant in situations where only physical copies are accessible: for instance, in circumstances where a significant number of records necessitates verification. However, the inherent dynamic characteristics of the signing act are lost due to the process of digitization.  
In the context of online (dynamic) verification, a touchscreen tablet or similar capturing equipment is utilized for recording the user's signature. While the offline strategy only relies on a two-dimensional representation of the signature, the online method involves supplementary data and distinct techniques for capturing aspects such as velocity, acceleration, stylus position, stroke order, and pressure over time [5]. In contrast to offline techniques, online methods have the advantage of acquiring additional dynamic signing information, hence enhancing their reliability.  
In the following section, a comprehensive overview of pertinent research studies addressing online signature verification is provided.