## 5 Conclusions  
The proposed apparatus for collecting handwritten biometric data relies on the integration of various types and categories of sensors and devices. The solution combines a multitude of sensors, including an accelerometer, gyroscope, magnetometer, piezoelectric sensor, camera, and touch screen. Furthermore, apart from the different sensors, devices such as tablets, smartphones, and smartwatches are also encompassed. The apparatus gathers data from sensors as the user writes with a stylus or finger on the tablet screen. The primary function of the equipment is to gather biometric characteristics by integrating different sensors to effectively authenticate users. The verification of the user by the writing recognition of signatures, phrases, words, or letters is one of the conceivable possibilities that might be carried out with this equipment. The future objective of the study is to examine which specific sensor or combination of sensors enhances the precision in differentiating users based on their handwriting. A controlled experiment was conducted involving five participants who had been instructed on the operation of the apparatus beforehand. No data were collected during the initial phase of becoming acquainted with the system's operation. Once the users were familiarized with the equipment, they were asked to write several handwritten signatures, phrases, words, and letters. When writing on the tablet screen, individuals used both a stylus and their index fingers. Preliminary results suggest that there is sufficient distinction between the data obtained from different participants. Additionally, there is a discernible uniformity among the individuals when they repeatedly write the same sentence, word, or particularly their signature. This information provides a foundation for further research on user authentication via multiple sensors, in order to achieve enhanced accuracy and robustness. Sensor readings from the magnetometer, accelerometer, and gyroscope reveal distinct differences between intra-person (within the same user) and inter-person (between different users) distances, demonstrating their classification potential in identifying users. This can be clearly observed in the tables displaying the DTW metric, as well as in the accompanyinggraphs, which further highlight the capability of these measurements in differentiating between users. These distinctions also hold for both stylus and finger input modalities. However, it is important to note that the apparatus is designed to capture comprehensive handwriting data using multiple sensors, and the analysis provided is centered on evaluating intra-person and inter-person variability rather than classification accuracy. Since this study does not focus on classification tasks or accuracy benchmarks, it is not feasible to provide concrete quantitative measures of accuracy improvement compared to other studies. Future research will add a classification framework, using benchmarks and error metrics like FAR, FRR, and EER to assess performance, allowing for a detailed comparison with other systems. Furthermore, it is possible to adjust the apparatus by having a smaller or greater number of devices or sensors and then see the impact these modifications have on the findings. Enhancing the S Pen Remote SDK's limitations or resolving the inability to link a smartwatch to a tablet will undeniably improve the overall performance and functionality of the system. The emphasis of future study efforts will be on increasing the number of test candidates in order to collect a substantial dataset. Following that, a comprehensive examination of the data will be conducted to substantiate the feasibility of sensor fusion-based user authentication during touchscreen writing. Prior to the production stage, state-of-the-art methods and related protocols for biometric template protection and their secure storage will be considered and subsequently used across all steps of the processing workflow.