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

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.

Reconstructing Three-Dimensional Human Poses: A Combined Approach of Iterative Calculation on Skeleton Model and Conformal Geometric Algebra

“…The image shown in Figure 11b corresponds to the located height error of the above joint points. As shown in the figures, the range of all the located error pixels is (3,8).…”

“…Recently, owing to extracted depth information, depth cameras [2,3] are applied for estimating 3D human poses and representing human activity. Kong et al [4] presented a hybrid framework to detect joints automatically based on a depth camera.…”

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

“…In order to discuss the performances more convincingly, two widely-accepted metrics called precision rate P and recall rate R [27] are selected to measure the detection results quantitatively. As is illustrated in Figure 11, an assumption is made that N T is the pixel number of true targets existing in the current frame; N D is the pixel number of targets detected by the tested algorithm, and N C = N T ∩ N D is the pixel number of targets detected correctly.…”

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

“…This has opened the door to the detection of abnormal events in an automatic manner. Fall detection is by far the most commonly faced challenge and the top topic in health environments [15,16], but there exist other challenges like monitoring Parkinson's disease [17] or even recognising emotional states [18,19].…”