# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 4 Kinect-Based Fall Detection Approaches
### Preprocessing  
The data acquisition module in turn sends the data to the preprocessing unit. This step ensures that the algorithms make the correct decisions. Firstly, we consider this stage crucial to refining and preparing the parameters. Different filters and techniques are used to clean up the signals and accentuate certain information. We found that filtering was widely used in depth data. We will not go into detail about each technique as we aim to identify the broad outlines applied in this step. Indeed, preprocessing refers to all the tasks that generate information as independent as possible of the defects specific to the Kinect and the experimental configurations.  
(1) In image preprocessing, different algorithms often detect the target person appearing and reappearing in Kinect's field of vision. They extract the silhouette in a video sequence and follow different body parts. The segmentation processes used are grouped into adaptive and non-adaptive methods for detecting the background and foreground of the image. Filters are primarily designed to reduce noise while preserving the definition and structure of the data. Its objective is to obtain a set of connected pixels having a form that can be analyzed facially. The morphological methods (erosion, dilation, opening, and closing) are applied after the binarization of images. They modify the structure and shape of regions of interest in frames. These nonlinear operators are applied to remove or fill pixels as needed. The normalization process is used to modify the input data into a more informal pixel intensity range. Researchers have applied the change in the Cartesian  
Figure 3: General flowchart of Kinect-based fall detection approaches.  
image coordinate system to the polar coordinate system [42]. The main advantage of this change is that it solves the nonlinear deformation of the pattern. The median, averaging, particle, and Kalman filters are deployed to homogenize and smooth areas. The median filter retains proper contours compared to the others. Framing objects by encompassing geometric shapes is used in particular works. Authors have relied on the coordinates within a predefined coordinate system to delimit the external surface of the objects of interest in the scene and identify their locations. To measure specific geometrical features compared to the ground, researchers have used techniques such as the v-disparity, Hough transformation, and least squares, as well as using them as an alternative to the parameters of the floor plane equation delivered by the SDK.  
(2) The large quantity and diversity of the dataset require a split step. Researchers have divided the data into two minimal parts according to predefined objectives. The training data is the largest, being used to train the algorithms and to calculate parameters in the case of thresholding algorithms. The test data are used to verify the performance of the deployed approaches, and must be made up of new data not used when designing and adjusting the model. Usually, the best performance is obtained with a ratio of 70-80% for training and 20-30% for testing [43; 44].