## 1 Introduction
The developed two-step algorithm simplifies calibration and localization, improving accuracy and reducing computational complexity compared to existing methods [51]. In the field of wireless sensor networks and the use of artificial intelligence techniques, a comprehensive discussion of recent research on area coverage, moving object localization, and WSN optimization is reviewed. The authors point out the need to optimize routes for localization and develop multicriteria models to resolve issues in this area [52]. In aquaculture, sensor data fusion has been applied for temperature monitoring in ponds, which is critical to ensuring the health and growth of aquatic species [53]. Additionally, sensor data fusion is used in robotics, particularly for simultaneous localization and mapping (SLAM) in underground power pipeline galleries [54]. Moreover, multi-source data fusion has been employed to diagnose rolling bearings based on an improved multiscale CNN [55]. In the educational domain, sensor data fusion has been used to improve affect detection in game-based learning through multimodal data fusion [56]. Structural health monitoring (SHM) sensors are proposed for damage diagnostics of composite panels, encompassing four levels: detection, localization, type, and severity of damage, with the aim of implementing condition-based maintenance (CBM) schemes [57]. Data-fusion algorithms inspired by fuzzy entropy have been proposed to improve the reliability of WSNs [58]. Multiprotocol mobile agents have also been used to design and analyze data-fusion schemes in mobile WSNs [59]. The designed system's hardware and software would need to comply with the human-gait analysis device requirements. The OneStep application uses an inertial measurement device with a sample rate of 100 Hz [60]. This sample rate is standard for many motion and gait analysis applications but does not significantly affect or increase the detail of the information because dynamic gaits are smaller. Shimmer sensor platforms [61] use a frequency of about 100 Hz for acceleration measurements. The autonomous sensing unit recorder system uses a sample rate more than 100 Hz to record data [62], but increasing the data collection rate does not provide significant benefits when studying detailed characteristics. Nevertheless, it is essential to note that measurement accuracy is often considered more important for analysis than sampling rate. In an article [63], it is concluded that a sampling rate as low as 17 Hz yields sufficient results in experiments of human-body movement monitoring. In general, measurement accuracy has a much greater impact than high sampling-rate values, as it is essential for adequate analysis and interpretation of results in the context of medical and research applications. Real-life engineering tasks often deal with special or original equipment, the absence of synchronizing data features, and the implementation of modern network protocols, widening the area of original device implementation. The problem of sensor data aligning with respect to time, when data are obtained using independent devices, requires such a solution. One of these tasks often occurs in the medical or health monitoring area, when there are inconvenient devices to install to inform human (sometimes on animal) limbs without a wire connection. This paper is dedicated to testing human-gait monitoring systems under real conditions using original sensors and independent data-acquisition systems. For the synchronization of independent data-acquisition systems, two simple methods were proposed: a solution involving repeated synchronization of the entire system by issuing network requests to extract sensors' data with very low latency and an alternative method where a synchronizing message is sent only at the beginning of a data-logging session, leaving only part of the system synchronized.