Human Activity Dataset of Top Frequent Elderly Emergencies for Monitoring Applications using Kinect

Gathering a dataset is essential in machine learning to develop new approaches and test their effectiveness. Despite their importance, it is difficult to find available datasets that meet researcher requirements, especially for the healthcare domain. This paper presents a new visual dataset of the top frequent elderly emergencies (heart attack, stroke, and Chronic Obstructive Pulmonary Disease (COPD) exacerbation). It is designed for the evaluation of different computational computer vision monitoring applications and human activity analysis. It can also be used for abnormal activity detection. This dataset covers the most frequent symptoms of each emergency. Each symptom is presented by different scenarios validated by a medical emergency expert. This dataset is recorded using the Kinect camera. Its samples are performed only in-depth and skeleton data to preserve the privacy of participants. Also, it is evaluated using methods based on Kinect skeleton data.