This project presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred.
Computer-vision based fall detection is a very important application that has been used to save lives. A fall occurs when a person accidently falls/slips while walking or standing. Age is a significant factor that is closely linked to severe falls. Several studies have shown that elderly people experience at least one fall every year. Also, falls are the main cause of accidental death in older adults aged 65 or older.
A fall may be due to health and aging-related issues, abnormality of walking surface or even lack of concentration. A falling person requires immediate assistance after the incident. Therefore, an effective fall detection system should accurately and robustly detect a fall when it occurs, without false detection (e.g. lying on the floor for the purpose of an exercise) for application in the general population.
This work includes a real time algorithm that utilizes the human 3D bounding box, expressed in world coordinates. Depth data are acquired using infrared (IR) signal from kinect which is not affected by lighting conditions. Using the 3D bounding box our algorithm calculates the first derivative (velocity) of width, height and depth to determine whether a particular activity is a fall or not. This algorithm does not require any pre-knowledge of the floor plane coordinates or the tracking of the particularly body part as the system do.