# Cross-Platform Gait Analysis and Fall Detection Wearable Device
## 4 Results  
To avoid the ethical permission in our experiment, the experimental results were executed by the laboratory members not the real patients and the wearable device should be attached at the ankle [17; 18; 19; 20; 21; 22; 23; 24; 25]. The number of laboratory members was more than 20. In the step count (\(SC\)), the range of acceleration value was calculated by the first quartile and third quartile values based on quartile method firstly [17; 18; 19]. After excluding outlier value, the range of acceleration was calculated from \(-0.28\) to \(0.17\), as shown in Figure 14. Once the value of acceleration was between \(-0.28\) and \(0.17\), the step count was increased by 1. It showed that the cross-platform gait analysis and fall detection wearable device (CPGAFDWD) we proposed could calculate the step count (\(SC\)), accurately. The units of measurement along the x-axes of Figure 14 was microsecond. The units of measurement along the y-axes of Figure 14 was the \(y\) value of three-axis acceleration. The step distance (\(SD\)) was calculated by the maximal and minimal acceleration values of one step count with 4 th root calculation as in Equation (1), where \(A_{max}\) and \(A_{min}\) were denoted as the maximal and minimal acceleration values, and \(n\) was denoted as the total step count [20]. \[SD=\sqrt[4]{A_{max}-A_{min}}\times n \tag{1}\]  
The fall detection (\(FD\)) was determined by the maximal and minimal acceleration values. After the experimental results executed by the laboratory members with the wearable device we designed at the ankle, the maximal and minimal acceleration values were \(-2\) and \(2\). Once the value of acceleration was below \(-2\) or over \(2\), the alarm of \(FD\) was triggered. As shown in Figure 15, the value of acceleration from time unit 191 to 210 was below \(-2\) or over \(2\). In this situation, the alarm of \(FD\) was triggered. It proved that CPGAFDWD could detect the fall detection (\(FD\)), accurately. Figure 14: Acceleration for step count. The accurate rate of \(SC\), \(AR_{SC}\), was defined as in Equation (2), where \(N_{SC}^{D}\) was defined as the number of detected \(SC\) and \(N_{SC}^{C}\) was defined as the number of actual correct \(SC\). The error rate of \(SD\), \(ER_{SD}\), was defined as in Equation (3), where \(N_{SD}^{D}\) was defined as the number of detected \(SD\) and \(N_{SD}^{C}\) was defined as the number of actual correct \(SD\). The accurate rate of angle estimation, \(AR_{Angle}\), was defined as in Equation (4), where \(N_{Angle}^{D}\) was defined as the number of angle estimation and \(N_{Angle}^{C}\) was defined as the number of actual angle. The accurate rate of \(FD\), \(AR_{FD}\), was defined as in Equation (5), where \(N_{FD}^{D}\) was defined as the number of detected \(FD\) and \(N_{FD}^{C}\) was defined as the number of actual correct \(FD\). In each performance metric, such as \(AR_{SC}\), \(ER_{SD}\), \(AR_{Angle}\), and \(AR_{FD}\), was 20 times in a round. Each experimental result was 10 rounds. Hence, it showed that CPGAFDWD we proposed could calculate the angle, accurately. Moreover, it also showed that CPGAFDWD we proposed could detect the fall, accurately. \[AR_{SC}=\frac{N_{SC}^{D}}{N_{SC}^{C}}\times 100\% \tag{2}\]  
\[ER_{SD}=\frac{\left|N_{SD}^{C}-N_{SD}^{D}\right|}{N_{SD}^{C}}\times 100\% \tag{3}\]  
\[AR_{Angle}=\frac{N_{Angle}^{D}}{N_{Angle}^{C}}\times 100\% \tag{4}\]  
\[AR_{FD}=\frac{N_{FD}^{D}}{N_{FD}^{C}}\times 100\% \tag{5}\]  
In the experimental results, the units of measurement along the x-axes of Figure 15 was microsecond. The units of measurement along the y-axes of Figure 15 was the \(y\) value of three-axis acceleration. It showed that \(AR_{SC}\) was up to 92%, 95%, and 89%, while \(N_{SC}^{C}\) was 10, 15, and 20, respectively. The average \(AR_{SC}\) by CPGAFDWD was higher than the average \(AR_{SC}\) by SVM step classifier [7; 20]. \(ER_{SD}\) was up to 4%, 8%, and 4%, while \(N_{SC}^{C}\) was 10, 15, and 20, respectively. The average \(ER_{SD}\) was below 6%.