## 3 Results  
### 1 st Data (SVM) Analysis  
The common method of recognizing activities using a three-axis accelerometer sensor involves converting the signal values by applying Equation (1) or a statistical formula or using a mathematical algorithm for activity classification [13; 14]. Equation (1) can be applied to determine the SVM, by extracting a single representative value from the X, Y, and Z accelerations output by the three-axis accelerometer sensor. This quantity can be used to offset the directional component of the acceleration along each axis that is caused by gravity and to collect fixed pattern information [15]. Equation (1) can be applied to determine the SVM, by extracting a single representative value from the X, Y, and Z accelerations output by the three-axis accelerometer sensor, where the SVM is an Euclidean Norm and each a\({}_{\mathrm{x}}\), a\({}_{\mathrm{y}}\), and a\({}_{\mathrm{z}}\) are the vector magnitude values attained from the three-axis accelerometer sensor [13; 14; 15]. \[\mathrm{SVM}\;=\;\sqrt{\mathrm{a}_{\mathrm{x}}^{2}+\mathrm{a}_{\mathrm{y}}^{2} +\mathrm{a}_{\mathrm{z}}^{2}} \tag{1}\]  
Figure 8 depicts results obtained by applying Equation (1) to the X, Y, and Z acceleration outputs corresponding to standing, walking, and running when the safety helmet is worn properly with the chinstrap fastened. Figure 8: Signal vector magnitude (SVM) during standing, walking, and running when the safety helmet is worn properly. Figure 7: Screenshots of the personal smartphone app for transmitting data. The horizontal axis in Figure 8 is the number of data items listed sequentially, and 100 data items were diagrammed. The vertical axis is an index that shows the SVM. Standing, walking, and running, which are typical activities in activity recognition, clearly exhibit a variety of differences following the application of Equation (1) [16]. Among the three activities, running, which involves relatively large body movements, has the largest SVM. Standing, which entails relatively little body movement, has the smallest SVM. Although the data were classified well by activity, there was some uncertainty regarding whether the helmet was worn properly with the chinstrap fastened or worn improperly with the chinstrap incorrectly fastened during each activity, as shown in Table 2. Table 2 summarizes the results of tests performed by dividing the three activities in [Figure 8] into states with the chinstrap fastened (On) and not properly fastened (Off) and then using the RandomTree algorithm of WEKA to classify 100 data samples. RandomTree is a Supervised Classifier; which is an ensemble learning algorithm that a large number of individual learners [17]. It is a typical decision-making tree data mining algorithm and it forms the basis of RandomForest [18]. Table 2 shows that the mean accuracy of the decision regarding whether or not the safety helmet is being worn properly during standing, walking, and running is 42.3%. The activity classification accuracy is high, but the accuracy of the classification regarding whether or not the safety helmet was worn properly during each activity is not as high. As an example, Figure 9 shows the walking activity, for which the determination of whether or not the safety helmet was worn properly has a particularly low accuracy among the activities in Table 2.  
As shown in Figure 9, the SVM of the walking activity when the safety helmet was worn properly is larger than when the safety helmet was not worn properly. However, it is