A Depth-Based Fall Detection System Using a Kinect® Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 2; pp. 2756 - 2775
• Main Authors: Gasparrini, Samuele, Cippitelli, Enea, Spinsante, Susanna, Gambi, Ennio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.02.2014; Molecular Diversity Preservation International (MDPI)
• Subjects: Algorithms; Cameras; depth frame; elderly care; fall detection; human recognition; Human subjects; Kinect; Sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140202756

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.