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Appl. Sci. 2017, 7(4), 316; doi:10.3390/app7040316

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

School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan
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Academic Editors: Plamen Angelov and José Antonio Iglesias Martínez
Received: 1 February 2017 / Revised: 17 March 2017 / Accepted: 22 March 2017 / Published: 24 March 2017
(This article belongs to the Special Issue Human Activity Recognition)
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Abstract

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. View Full-Text
Keywords: fall detection; partially-observed videos; fall prediction; view-invariant geometric descriptor; support vector machines; human representation; Kinect sensor fall detection; partially-observed videos; fall prediction; view-invariant geometric descriptor; support vector machines; human representation; Kinect sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alazrai, R.; Momani, M.; Daoud, M.I. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation. Appl. Sci. 2017, 7, 316.

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