Unobtrusive Fall Detection Using 3D Images of a Gaming Console: Concept and First Results

Marzahl, Christian; Penndorf, Peter; Bruder, Ilvio; Staemmler, Martin

doi:10.1007/978-3-642-27491-6_10

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…When the sensor cannot evaluate the depth information for some pixels, as those corresponding to corners, shadowed areas, and dark objects, it assigns them a null depth value. There are various approaches to handle null depth values: differently from [ 9 ], where the null values are discarded, in [ 14 ] the authors propose a substitution process. In [ 15 ], a so-called “flood-fill” algorithm is used to resolve this problem, while in this work the null pixels are replaced by the first valid depth value occurring in the same row of the frame.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In [ 9 ], the Kinect ® sensor is placed on the floor, near a corner of the bedroom. A restriction of this setup is the limited coverage area, caused by the presence of the bed.…”

Section: Related Workmentioning

confidence: 99%

“…Taking into consideration the techniques described above, our solution has the following advantages: the top view depth frames are directly available, without the need of a transformation process applied to the spatial coordinates; the direct top view allows a better monitoring of the scene, than the ones in [ 9 , 13 ], and the occlusion phenomenon is therefore reduced; avoiding a machine learning solution in our approach strongly reduces the computational demand; the algorithm is portable on different hardware platforms, as it works on raw depth data, possibly captured by different sensors, not only Kinect ® . This is not the case for the system proposed in [ 10 ], which is bound to the NITE 2 middleware.…”

Section: Related Workmentioning

confidence: 99%

“…the direct top view allows a better monitoring of the scene, than the ones in [ 9 , 13 ], and the occlusion phenomenon is therefore reduced;…”

Section: Related Workmentioning

confidence: 99%

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

Gasparrini

Cippitelli

Spinsante

et al. 2014

Sensors

185

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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.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…In [ 9 ], the Kinect ® sensor is placed on the floor, near a corner of the bedroom. A restriction of this setup is the limited coverage area, caused by the presence of the bed.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…the direct top view allows a better monitoring of the scene, than the ones in [ 9 , 13 ], and the occlusion phenomenon is therefore reduced;…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Gasparrini

Cippitelli

Spinsante

et al. 2014

Sensors

185

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show abstract

“…The results demonstrate the capability of the proposed approach to detect falls from fully-observed video sequences. In comparison, the Kinect-based approach proposed by Marzahl et al [35], which was evaluated using 55 fall depth-map videos, achieved an average fall classification accuracy of 93%. Similarly, the Kinect-based system introduced by Planinc and Kampel [36] achieved fall detection accuracies between 86.1% and 89.3% based on a dataset that includes 40 falls and 32 non-falls depth-map videos.…”

Section: Evaluation On the Fully-observed Video Sequences Scenariomentioning

confidence: 99%

Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

2017

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This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD). Furthermore, we have developed a histogram-based representation (HBR) based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine) classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93.6%, 77.6% and 65.1%, in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.

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Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

Stone

Skubic

2014

Smart Homes and Health Telematics

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Unobtrusive Fall Detection Using 3D Images of a Gaming Console: Concept and First Results

Cited by 14 publications

References 3 publications

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

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

Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

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