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Sensors 2014, 14(2), 2756-2775; doi:10.3390/s140202756
Article

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

* ,
,
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Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, Ancona 60131, Italy
* Author to whom correspondence should be addressed.
Received: 10 January 2014 / Revised: 30 January 2014 / Accepted: 8 February 2014 / Published: 11 February 2014
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Abstract

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.
Keywords: depth frame; elderly care; fall detection; human recognition; Kinect depth frame; elderly care; fall detection; human recognition; Kinect
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Gasparrini, S.; Cippitelli, E.; Spinsante, S.; Gambi, E. A Depth-Based Fall Detection System Using a Kinect® Sensor. Sensors 2014, 14, 2756-2775.

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