## 7 Results Discussion  
The system design process was conducted using the proposed DM, and based on validation results, the stakeholders and future users approved the solution and continued the project towards implementation and commercialization. The applied DM resulted in the system consisting of hardware and software components, shown in Table 1, suitable to realize required functionalities and fulfilling general constraints. The presented system validation concerned the crucial requirements of stakeholders and future users such as a four room-zones level localization accuracy, fall detection, activity recognition and classification of normal, suspicious and dangerous situations. The 1500-step test proved that the proposed step detection method, based on data gathered with the three-axial accelerometer and magnetometer, together with the signal magnitude vector and the pedestrian dead reckoning algorithm, shows a high robustness of 98%. The direction estimation test gave 1.33\({}^{\circ}\) of the mean uncertainty of the angular walking direction with a standard deviation of 1.15\({}^{\circ}\) and maximal error of 3\({}^{\circ}\). Such high accuracy and sensitivity confirm the usefulness of the method for indoor location monitoring. However, the further investigation indicated that small natural motions of the body during walking caused up to a 1-m error in the position estimation. Therefore, the 6\({}^{\circ}\) threshold filter was applied, which resulted in the reduction of uncertainty in position estimation by up to 40 cm. For the walk test path consisting of five sections performed back and forth and repeated three times, the mean localization uncertainty was 33 cm with a standard deviation of 18 cm where the maximal localization error was 66 cm. The same test's results indicated that for one round of the walk from the start to end points, the mean localization uncertainty was just 4 cm with a standard deviation of 2 cm. However, for a walk in reverse order, the mean value and standard deviation increased up to 40 cm and 12 cm, respectively. The detailed analysis of the test data resulted in the mean localization uncertainties for each round of 22 cm, 46 cm and 30 cm, with standard deviations of 20 cm, 12 cm and 11 cm for the first, second and third round, respectively. Moreover, average localization uncertainties for the forward direction are almost the same and equal about 21 cm, and the same uncertainty for the back direction is about double and equal to almost 40 cm. This means that the proposed localization algorithm has a small localization uncertainty, and the most distortive movements are turns. It seems that the turns are the cause of the increasing localization uncertainty. The test results verified that the designed method is sufficient for the four room-zone level accuracy requirement. Analysis of test results of forward fall accelerations in the \(x\)-, \(y\)- and \(z\)-axes and SMV, showing the characteristic peaks concerning the beginning of the fall and the contact with the floor, was applied to define the signal thresholds used to recognize the fall. Moreover, the acceleration change in the \(z\)-axis, which the gravity force influences the most, indicating the lying position of the monitored person, is used for the fall confirmation. The performed tests proved that the designed method of fall detection is valid in 98% of cases. The data gathered during standing and sitting activities indicated that the observed differences in accelerations in the \(x\)-, \(y\)- and \(z\)-axes and SMV are not sufficient to distinguish between these two activities. However, possible identification of getting up and sitting down activities can be used to recognize standing and sitting activities. The beginning of the getting up activity causes the acceleration in the \(z\)-axis; hence, SMV changes quickly, and at the end of the activity both acceleration in the \(z\)-axis