## 4 Conclusions  
After performing the experimental research, it was noted that the command and data-transfer delay during the test falls into the desirable range. The latency of commands to collect and transfer sensor data does not exceed 1.33 ms and 1.7 ms, respectively. The total duration of gathering, transmitting, analyzing, and exporting data from three piezoresistive pressure sensors and an accelerometer with a gyroscope module lies in the range of 10.5 ms, with the data-acquisition success rate not falling below 80%. Thus, the research confirms that the use of the MPU6050 module, pressure sensors, and transmitting and receiving ESP32 microcontrollers, together with the developed data-synchronization technique, enables reasonably reliable and fast data acquisition from several different sensors that are located up to 100 m away from the master controller in an open space or separated by a solid concrete wall. The achieved characteristics of such a data-gathering system open wide opportunities for applying this method in the field of gait analysis. Experimental research revealed that the average desynchronization of data gathered from separate data-module sets did not exceed 1 ms, and the average desynchronization of the data-module sets and the data-reception unit did not exceed 2.5 ms. As tight synchronization was achieved, the proposed method is deemed practical in systems where a data-storage device and data-gathering devices must be accurately synchronized in a wireless system. The achieved sampling rate of 24.6 samples per second is sufficient for human-gait monitoring and analysis, assuming that humans step at approximately up to 5 Hz frequency. Thus, the device can be used for human-gait monitoring. For other applications with a higher sampling rate, more advanced and sophisticated technical and network solutions may be required. The alternative method of data-transmission mode has better data-transmission latency but indicates lower stability at higher distances or in the presence of obstacles. The decision to use data synchronization with the first method has a solid reason of having higher stability. Additional research and development could be directed at improving the filtering and synchronization algorithms, accounting for additional factors, such as noise and temperature variations, and extending this method to other types of sensors and platforms. This will help achieve even greater accuracy and reliability in data synchronization and expand its application to various areas of research and development. The alternative synchronization method could be improved to outperform the proposed synchronization method in terms of lower latency and matching stability. An intermediate device in data transmission could be implemented into the system, overcoming the current distance and obstacle limitations, and allow for a wider application of the system. Currently, relatively simple data-analysis algorithms for human-gait analysis could be developed further to verify whether the sampling rate of the system is sufficient for a more involved analysis. Conceptualization, V.B. and A.D.; methodology, D.C.; software, D.C. and G.B. ; validation, U.P., A.D. and G.B. ; formal analysis, G.B. ; investigation, V.M. ; resources, V.B. ; data curation, V.M. ; writing--original draft preparation, V.M. ; writing--review and editing, A.D. and U.P. ; visualization, V.M. ; supervision, V.M. ; project administration, A.D.; funding acquisition, V.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is part of the AI4 DI project, receiving funding from the Electronic Components and Systems for European Leadership Joint Undertaking in collaboration with the European Union's H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement n\({}^{\circ}\) 826060. **Data Availability Statement:** The data can be shared up on request. The data are not publicly available due to internal consortium regulations.