No one refutes the importance of datasets in the development of any new approach. Despite their importance, datasets in computer vision remain insufficient for some applications. Presently, very limited autism datasets associated with clinical tests or screening are available, and most of them are genetic in nature. However, there is no database that combines both the abnormal facial expressions and the aggressive behaviors of an autistic child during Meltdown crisis. This paper introduces a Meltdown Crisis, a new and rich dataset that can be used for the evaluation/development of computer vision-based applications pertinent to children who suffer of autism as security tool, e.g. Meltdown crisis detection. In particular, the ”MeltdownCrisis ” dataset includes video streams captured with Kinect which offers a wide range of visual information. It is divided on a facial expressions data and physical activities data. The current ”MeltdownCrisis ” dataset version covers several Meltdown crisis scenarios of autistic children along various normal state scenarios grouped into one set. Each scenario is represented through a rich set of features that can be extracted from Kinect camera.
In this study, researchers describe the setting and steps for building Meltdown crisis scenario datasets. The proposed MeltdownCrisis dataset is a feature-based and it covers all the stream types provided from the Kinect: color, body (skeleton), depth, infrared and body index. To facilitate its exploitation, the dataset is organized in terms of scenarios covering 13 specific case of meltdown crisis scenarios: it covers 23 videos for children are in normal state, 18 videos for children in postcrisis state and 18 videos for children in meltdown crisis. A set of features from our MeltdwownCrisis dataset is evaluated using many classifier algorithm and the best results obtained by RF algorithm classifier.