## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Participants** & \multicolumn{4}{c}{**Visualization Inspection**} & \multicolumn{4}{c}{**Polynomial Value**} \\ \hline \hline  & **Normal Speed** & \multicolumn{2}{c}{**Fast Speed**} & \multicolumn{2}{c}{**Normal Speed**} & \multicolumn{2}{c}{**Fast Speed**} \\ \hline \hline  & **Step Length** & **SD** & **Step Length** & **SD** & **Step Length** & **SD** & **Step Length** & **SD** \\ \hline Subject 1 & 0.5604 & \(\pm 0.0943\) & 0.7495 & \(\pm 0.0896\) & 0.6008 & \(\pm 0.0501\) & 0.7830 & \(\pm 0.0563\) \\ \hline Subject 2 & 0.5077 & \(\pm 0.0355\) & 0.6107 & \(\pm 0.0845\) & 0.5086 & \(\pm 0.0353\) & 0.5690 & \(\pm 0.0969\) \\ \hline Subject 3 & 0.5771 & \(\pm 0.0234\) & 0.6238 & \(\pm 0.0701\) & 0.5834 & \(\pm 0.0637\) & 0.6297 & \(\pm 0.0930\) \\ \hline Subject 4 & 0.5888 & \(\pm 0.3786\) & 0.6447 & \(\pm 0.0326\) & 0.5838 & \(\pm 0.0473\) & 0.6644 & \(\pm 0.0455\) \\ \hline Subject 5 & 0.6351 & \(\pm 0.0836\) & 0.7597 & \(\pm 0.0705\) & 0.6662 & \(\pm 0.0298\) & 0.7394 & \(\pm 0.1188\) \\ \hline Subject 6 & 0.6145 & \(\pm 0.0597\) & 0.7234 & \(\pm 0.0768\) & 0.6398 & \(\pm 0.0618\) & 0.7437 & \(\pm 0.0615\) \\ \hline Subject 7 & 0.6121 & \(\pm 0.0163\) & 0.5991 & \(\pm 0.1104\) & 0.6116 & \(\pm 0.0104\) & 0.6109 & \(\pm 0.1529\) \\ \hline Subject 8 & 0.5751 & \(\pm 0.4267\) & 0.7376 & \(\pm 0.0593\) & 0.6174 & \(\pm 0.0568\) & 0.7970 & \(\pm 0.0687\) \\ \hline Subject 9 & 0.6262 & \(\pm 0.1082\) & 0.7393 & \(\pm 0.2300\) & 0.6659 & \(\pm 0.0427\) & 0.8327 & \(\pm 0.0664\) \\ \hline Subject 10 & 0.4758 & \(\pm 0.0804\) & 0.6956 & \(\pm 0.0571\) & 0.4707 & \(\pm 0.0919\) & 0.6826 & \(\pm 0.0715\) \\ \hline Average & \(\pm 0.0515\) & & \(\pm 0.0625\) & & \(\pm 0.0633\) & & \(\pm 0.0874\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The standard deviation (SD) of step length in comparison with the quadratic polynomial curve fitting value and the visually detected value. In comparison, the dual-laser range sensor results reported in [45] achieved an average error of 10% or 10 cm. The current setup, though using a single LiDAR, achieved results comparable to those of the dual sensors. The proposed method in this study presented an improvement accuracy of 4% using the result as a standard in normal walk. In addition, with the aim of non-obstructive gait step length measurement on the subject, this proposed LiDAR-based approach achieved the intended purpose. Overall, using the polynomial best-fitting curve for the cloud points reported promising results individually than when using the visual inspection method. Subject 9 had the highest standard deviation by using the visual inspection than using the quadratic polynomial fitting value approach. This is because, since the heel-shaped contour of the cloud points is incomplete as the subject walks faster and farther away from the device, it is difficult to determine the estimated position of the heel of the leg. By using the curve-fitting approach, the incomplete heel shape of the cloud points can be achieved by estimating the heel position of the leg. In Figure 11, the result shows that the proposed method of polynomial estimation of the heel cloud point is able to detect the foot contact and foot off during walking from both the left leg and right leg. A similar pattern of a shorter step length in females than males, as seen in Figures 12 and 13, has also been reported in [48]. The differences in step length may be related to the differences in body proportions between men and women. In Figure 14, the association between the step length and the subject's height for normal walk has a very low correlation while, for fast walk, it is considered to be moderately correlated. There is little significant relationship between the height of the subject and step length. In adults, there is no relation between step length and height at normal walking speed; however, there is a significant difference in step length given the lower extremity length [49].