## 1 Introduction  
Gesture recognition systems using IMU (Inertial Measurement Unit) sensors are solutions in which relatively large amounts of data are processed. Thus, the efficiency of such systems is related to the methods of processing data from sensors and their transmission. Wearable solutions for monitoring movement or other body parameters use radio data transmission [1; 2; 3; 4], including transmission in the ISM (Industrial, Scientific, Medical) 2.4 GHz band. Within this band, radio transmission is implemented in many standards, such as Wi-Fi (IEEE 802.11) [5; 6], Bluetooth (IEEE 802.15.1) [7; 8] or ZigBee (IEEE 802.15.4) [9; 10]. Here, it is important to point out the key problems related to the processing and radio transmission of data in sensor networks. Devices using radio transmission in these technologies may be in close proximity and interfere with each other [11]. Moreover, the use of a large number of wireless IMU systems can lead to their mutual interference. This causes a decrease in the efficiency and reliability of transmission, which reduces the functionality of these systems. For this reason, it is important to reduce radio band occupancy, for example, by limiting radio transmission. Radio transmission also has a significant impact on the energy demand of sensor systems, therefore, limiting the use of telerransmission modules allows for a reduction in electricity consumption. In turn, reducing energy demand is of particular importance in the case of solutions using battery power supply. There are many different methods of radio transmission control to optimize network traffic or reduce energy consumption. The latter can be achieved by monitoring the network and controlling the frequency of connection events (mainly in Bluetooth LowEnergy), transmission speed, and transmitter power [12; 13]. In turn, the transmitter power can be controlled based on the input data from the system sensors [14]. Transmission control using the input data can be implemented using machine learning [14]. One of the methods for reducing teletransmission band occupancy is data aggregation. In the Wi-Fi standard (802.11), a mechanism for sending data blocks containing several data frames has been introduced, wherein the receipt of the data block is acknowledged by one ACK (Acknowledgment). Controlling the length of data blocks can be used to optimize network traffic while maintaining transmission reliability [15]. The network radio traffic can also be limited by dividing the network into clusters with a master node as well as assigning different priorities to data and data queuing [16]. Solutions modifying MACs in terms of prioritizing data are also proposed [17], and time slots of variable length [18] or dedicated slots for critical data transmission are introduced [19]. Modifications of the MAC layer require appropriate implementation in all cooperating network devices. Optimization of radio network management is also carried out by modifying the routing protocols by introducing specialized supervision devices [20]. Such solutions involve sending additional information about the packet path and implementing algorithms, e.g., ant colony [21] or fuzzy logic [22]. In the case of coexistence of several networks with many access points, optimization methods are introduced that make it possible to select an access point for the network device, which is used, for example, in cellular networks [23]. Reducing the network load may also be realized by using two networks operating in separate bands in parallel (for example Wi-Fi and LTE/3G) [24; 25]. Radio data transmission can also be optimized by limiting the data packet length, for example, by sending only differences between consecutive values, which may limit the scope of sent data and allow the original data to be restored [26]. In order to reduce the energy expended on radio transmission and limit the amount of transmitted data, the measurement signal sampling frequency is controlled in sensor systems.