# Study on Elevator Recognition Techniques for Upper-Limb-Disabled Wheelchair Users
## 2 Materials and Methods
### LiDAR-Sensor-Based Gate Structure Shape Recognition Method
#### 2.2.1 Filtering to Improve Elevator Recognition Rate  
The width of the elevator gate is narrow, so to pass safely considering the width of the electric wheelchair, accurate coordinates of the elevator door frame must be detected for safe driving [14]. Therefore, in order to accurately detect the coordinates of the elevator, a low-pass filter (LPF) is applied to remove noise from the raw data collected from LiDAR. An LPF is used to remove a high frequency signal from the data components in the point-cloud generated by the LiDAR sensor. The relational equation is shown in Equation (1):  
\[\overline{x_{k}}=W\overline{x_{k-1}}+(1-W)x_{k}\,\ \ (0<W<1) \tag{1}\]  
This study proposes an algorithm to recognize the shape of the elevator in the collected point-cloud. However, if there is an object similar to an elevator within the point-cloud, there is the possibility of misrecognition. In addition, when measuring high-reflective materials, these can be measured differently from actual distance information due to their high surface reflectance, which is an obstacle to the recognition algorithm process. Therefore, it is necessary to apply a technique of masking the data based on the distance value measured from LiDAR to prevent misrecognition of elevator-like forms and unnecessary noise. The masking technique is a method of removing unnecessary data by treating it as null when the measured distance value is greater than the value of \(threshold_{max}\) or less than the set value of \(threshold_{min}\), as shown in Equation (2). The threshold value is set in consideration of the position where the wheelchair user presses the elevator call switch. \[x_{n}=null\,\ \ (x_{n}>threshold_{max}\ \ ||\ \ x_{n}<threshold_{min}) \tag{2}\]  
2.2.2 Clustering and Line Segment Extraction Techniques for the Recognition of Gate Structure Shapes  
In order to perform the elevator recognition technique, it is necessary to analyze point data in the point-cloud. This can be implemented though a segmentation process for extracting a set of points with information on the elevator gate, and a line segment extraction process to perform a test of the elevator gate recognition conditions for each set. Segmentation is a data-processing technique that divides a given data set into several areas, and algorithms should be selected appropriately according to the acquisition environment and the purpose of clustering. Segmentation algorithms typically include K-means, DBSCAN (density-based spatial clustering of applications with noise), and ABD (adaptive breakpoint detector). A general clustering method performs area division based on a threshold value in which the Euclidean distance between consecutive points is fixed [20]. However, since this method has the problem that it is not formed as a single cluster for areas with low point data density, points belonging to the elevator may not be formed as a single segment due to the position of the LiDAR sensor determined by wheelchair user manipulation. Among the ways to improve the limitations of the general clustering methods, the K-means algorithm is a method of clustering the collected data into k clusters, which is simple and fast [21; 22]. However, the number of clusters must be specified, which is not appropriate considering that the number of clusters created when scanning the elevator gate depends on the LiDAR location. DBSCAN is a method of forming clusters based on the density of points collected by a density-based clustering algorithm, and it does not require a predefined number of clusters and can detect clusters of various shapes and densities [22; 23; 24]. However, the DBSCAN algorithm needs to set a fixed bandwidth for setting the criteria for clustering. This is not suitable because the number of clusters according to the elevator shape may be inappropriately generated according to the position of the wheelchair.