# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 6 Kinect Datasets for Fall Detection
In the same experimental setup there are two fall detection benchmark datasets, namely, EDF and OCCU [119]; EDF has no occlusion data, while OCCU has occlusion data. Each of the five volunteers made an un-occluded fall from each point of view in eight directions. There are 110 falls and 30 actions that appear to show similar features to those of a fall occurrence. The video sequences are recorded at the same time from both viewpoints. The main characteristic of the OCCU is the presence of occluded falls with the end entirely occluded by a bed. The data are not captured simultaneously from the two Kinect cameras. The SDU fall dataset [92] was recorded using one version 1 Kinect camera installed at 1.5 m height. This dataset included five ADLs and intentional falls performed by ten young male and female subjects. In addition, there are a total of 1800 video clips collected in randomly experimental conditions, such as carrying or not carrying an object, lighting on or off, varied layout of the room, and changes of position and direction relative to the Kinect camera. The resolution of the datastream was 320 \(\times\) 240 pixels, and it is stored in an audio-video interleaved format at 30 fps. The SDU FAIL server is in service again, and a new access link with the password is available on the Google page of the author. The UR fall detection dataset [120] can be considered the wealthiest dataset in terms of data relating to vision and movement. This dataset comprises 70 event sequences; the measurements were collected using two Kinect cameras and accelerometers. All RGB and depth images are synchronized with movement data based on the timestamp value. Camera 0 was parallel to the ground at the height of around 1m, while Camera 1 used an in-ceiling configuration 2.5 m above the ground. Moreover, accelerometers were mounted on the waist or close to the pectorals. Five subjects performed the action of falling (falls from standing position and falls from sitting on the chair) and ADLs (walking, sitting down, lying on the floor, taking an object from or placing it on the floor, tying shoelaces, bending to left of an object and crouching down) in traditional spaces such as an office, classroom, etc. For the fall events, two cameras and accelerometer devices were used. ADLs were recorded only by the camera parallel to the floor with the accelerometer. The TST fall detection dataset v1 [58] was collected using a Kinect version 1 in top view configuration. It includes depth frames captured from four volunteers. A total of 20 tests were carried out by subjects aged 26 to 27 years with different heights between 1.62 and 1.78 m. The tests were organized into two main groups; the first ten tests include data from two or more people walking in the covered area, and the rest correspond to simulated falls. Moreover, the TST fall detection dataset v2 [61] provides vision and acceleration samples. In this dataset, eleven healthy volunteers performed eight types of activities three times, resulting in 264 sequences. The young subjects who performed The ADLs and falls actions were aged between 22 and 39 years and measured 1.62 to 1.97 m. TSTv2 was captured using a Kinect version 2 with two accelerometers (IMU shimmer) attached to the wrist and waist of the subjects. [PERSON] et al. [86] built the IRMTv1 dataset with two synchronized Kinects. This dataset provides the integer numbers matrices representing the distance between the plane parallel to the device lens and the image's scene points across both cameras simultaneously. Two performers simulated 20 different types of falls and 20 non-falls, for a total of 160 sequences of depth images. The length of the recorded data sequence corresponds to 75 frames. Each performer simulated the scenarios at a distance of approximately 1.5-4 m from each instrument in the observed area.