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Contributed by Author Contributions Samuele Gasparrini and Enea Cippitelli were responsible for the design, implementation and testing of the algorithms presented in the manuscript; Susanna Spinsante was involved in the discussion of the results and manuscript editing; Ennio Gambi coordinated the research project development.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license http: This article has been cited by other articles in PMC. 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. 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. Introduction One of the most important research activities related to the Ambient Assisted Living scenario is in the field of automatic fall detection.
It is well known that in the case of a fall event, enabling fast help intervention allows decreasing the probability of complications derived from physical damage due to the accident such as those caused by fractures of the lower or upper limbs [ 1 ].
In the last decades, several automatic fall detection techniques have been proposed. In [ 2 ], wearable sensors are used to provide information about critical movements. In [ 3 ], the authors used an RGB camera to monitor the environment, but in recent years depth cameras have become a new tool to exploit.
The availability of depth information allows one to implement simpler identification procedures to detect human subjects.
The advantages of this technology, with respect to classical video-based ones, are: This feature helps to keep identity confidential. Several commercial devices are able to provide depth data at a stable frame rate. The depth information is generated by analyzing the distortion of an infrared pattern, projected by the sensor and scattered by the surface of the intercepted objects.
This specific configuration does not allow it to activate known skeleton tracking tools, like the NITE middleware [ 6 ], or Microsoft Skeleton [ 7 ], provided by OpenNI libraries and Microsoft SDK, respectively, that are usually exploited in classical, front-view situations.
It is then necessary to develop an ad-hoc algorithm for automatic fall detection, based on the analysis of raw depth data provided by the sensor. The logical steps upon which the proposed solution is built are: In this phase, a reference frame is generated to help better identification of human subjects; distinguish object algorithm: In addition, it is able to manage blob fusion when two or more subjects get in contact.
The entire procedure is illustrated in Figure 1where each step described above can be identified. It might also be noted that the preprocessing and segmentation part is detailed with the outputs of every sub-step.
The paper is organized as follows: Section 2 provides an overview of related work about automatic fall detection using depth frames.The workforce is changing as businesses become global and technology erodes geographical and physical regardbouddhiste.com organizations are critical to enabling this transition and can utilize next-generation tools and strategies to provide world-class support regardless of location, platform or device.
Abstract: In this paper, we present a novel fall detection system based on the Kinect sensor.
Balls of Fury/Walk Hard/Talladega Nights A Syllabus of a Course in Elementary Physics (), Frederick E Sears Packaging in France - Strategic Forecasts to Darkling, Yasmine Galenorn, Cassandra Campbell Financial and Managerial Accounting, Jocelyn . Depth Sensor Based Skeletal Tracking Evaluation for Fall Detection Systems Subarna Sinha1, to evaluate the capability of Microsoft Kinect sensor for using in a fall detection system which determines automatically if a fall has occurred. 2. human activity analysis. A core part of the algorithm is described in the paper in which. Fukuoka | Japan Fukuoka | Japan.
The originalities of this system are two-fold. Firstly, based on the observation that using all joints to represent human posture is not pertinent and robust because in several human postures the Kinect is.
Abstract. This paper 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). Using Harvard Multiple works by the same author(s) in the same year. Identify works by the same author in the same year by adding letters a, b, c, and so forth, to the year in both the in-text reference and the reference list entry.
Vol.7, No.3, May, Mathematical and Natural Sciences. Study on Bilinear Scheme and Application to Three-dimensional Convective Equation (Itaru Hataue and Yosuke Matsuda).
Balls of Fury/Walk Hard/Talladega Nights A Syllabus of a Course in Elementary Physics (), Frederick E Sears Packaging in France - Strategic Forecasts to Darkling, Yasmine Galenorn, Cassandra Campbell Financial and Managerial Accounting, Jocelyn .