## 3 Results  
The results of experimental studies prove the comprehensive functionality of the developed human-gait-monitoring system consisting of three independent modules communicating via the local WIFI network. During the experiments, in the beginning, sensor data-acquisition modules (slave devices) were researched to determine whether the developed sensors, their installation, and calibration are suitable and whether the sampling rate is sufficient to notice gait irregularities by analyzing individual steps. Next, the data-transmission latency and the influence of various obstacles on the data-transmission process were defined. Data from pressure-sensor arrays consisting of three piezoresistive sensors are presented in Figure 5. There is a very frequent case of human gait, climbing the stairs, where the load of human weight is distributed to the foot sole. Such a case reveals gait defects very fast. At the beginning of the ascending stairs, the load on the metatarsal bones and the heel increases, after which it gradually begins to decrease. Such variation could suggest that, at the start of the ascent, the maximum portion of the load falls on all pressure sensors, after which the weight is transferred forward. There is an increase in the pressure sensor's pressure values at the end of the step, indicating that the load increases at the moment of lowering the foot. The values of the sensors that are unloaded have little meaning, while they represent the residual pressure on the insole rather than the partial load on the human body. Technically, unloaded insole data are not taken into account in gait analysis. The sample rate of the data-gathering equipment is sufficient to see repeating characteristics in step graphs: peaks, slopes, plateaus, etc. Evaluating the characteristics visually, for example, by noting the number of peaks, their size, and their position in relation to each other, is usually enough to determine gait abnormalities. For a more precise evaluation, distinct steps are extracted, and the most representative of them are used to generate an average step graph; when comparing two average step graphs, their same phase data points' values are compared. Instead of comparing entire average graphs, separately calculated characteristics, such as step length and left and right foot gait phase difference, rate of applying foot pressure, etc., can be compared instead. The behavior of the foot during flight to the next supportive place requires another type of sensor. In this case, an accelerometer with a gyroscope brings information about the movement of the raised foot. Data from these sensors when the foot is loaded are neglected  
Figure 5: Pressure-sensor data: (L1) yellow line—pressure sensor under tibial, (L2) blue line—pressure sensor under lateral plantar, (L3) green line—pressure sensor under medial plantar. as well. Acceleration values after filtering are suitable for human-gait analysis, but one must use numerical integration twice. After integration, displacement values are obtained, but, as usual, the integration procedure's problem is defining proper initial conditions. In this case, zero acceleration and zero displacement values were taken at the moment when the foot was fully pressed. This procedure is typically performed offline after the data from the experiment are collected; online analysis is not practical. The data from the gyroscope were used to register changes in the orientation of the footwear in space along Cartesian axes (Figure 6). The values of the X and Y axes are negative at the beginning of the movement, which may suggest that the shoe was tilted forward and to the right. These values quickly return to zero, indicating the shoe's return to its original position. The \(Z\)-axis values are positive at the beginning of the movement and then increase, which may suggest a shoe rotation around the vertical axis.