Fall detection using k-nearest neighbor classification for patient monitoring
01 Dec 2015-pp 520-524
TL;DR: A novel method to detect falls which combines four features, Orientation angle, ratio of fitted ellipse, Motion Coefficient, Silhouette threshold, which gives accuracy above 95% on stored video sequences of activities and real time environment is proposed.
Abstract: The incident of fall of elder people increases day by day. Falls are one of the greatest risks for seniors living alone. Sometimes people may get serious injury to the spinal cord and hip region. In such cases, an injured elder people may remain on the ground for several hours after a fall incident has occurred. So there is a need of fall detection system to avoid such incident. This paper propose a novel method to detect falls which combines four features, Orientation angle, ratio of fitted ellipse, Motion Coefficient, Silhouette threshold. These features act as inputs to K-Nearest Neighbor classifier which recognizes fall events. This algorithm gives accuracy above 95% on stored video sequences of activities and real time environment.
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TL;DR: Using fall motion vector, this work is able to efficiently identify fall events in varieties of scenarios, such as the narrow angle camera (Le2i dataset), wide angles camera (URFall dataset), and multiple cameras (Montreal dataset).
Abstract: Representation of spatio-temporal properties of human body silhouette and human-to-ground relationship, significantly contribute to the fall detection process. So, we propose an approach to efficiently model the spatio-temporal features using fall motion vector. First, we construct a Gaussian mixture model (GMM) called fall motion mixture model (FMMM) using histogram of optical flow and motion boundary histogram features to implicitly capture motion attributes in both the fall and non-fall videos. The FMMM contains both fall and non-fall attributes resulting in a high-dimensional representation. In order to extract only the relevant attributes for a particular fall or non-fall videos, we perform factor analysis on FMMM to get a low dimensional representation known as fall motion vector. Using fall motion vector, we are able to efficiently identify fall events in varieties of scenarios, such as the narrow angle camera (Le2i dataset), wide angle camera (URFall dataset), and multiple cameras (Montreal dataset). In all these scenarios, we show that the proposed fall motion vector achieves better performance than the existing methods.
67 citations
TL;DR: A smartphone-based architecture for the automatic detection of falls that incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision is presented.
Abstract: During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.
56 citations
TL;DR: A novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body is presented, which employs a new set of features to detect a fall.
Abstract: Falls are one of the greatest risks for older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body. The proposed approach employs a new set of features to detect a fall. Motion information of a segmented silhouette when extracted can provide a useful cue for classifying different behaviours, while variation in shape and the projection histogram can be used to describe human body postures and subsequent fall events. The proposed approach presented here extracts motion information using best-fit approximated ellipse and bounding box around the human body, produces projection histograms and determines the head position over time, to generate 10 features to identify falls. These features are fed into a multilayer perceptron neural network for fall classification. Experimental results show the reliability of the proposed approach with a high fall detection rate of 99.60% and a low false alarm rate of 2.62% when tested with the UR Fall Detection dataset. Comparisons with state of the art fall detection techniques show the robustness of the proposed approach.
55 citations
Cites methods from "Fall detection using k-nearest neig..."
...The study in [24] presents a novel method to detect falls which combines the orientation angle and the ratio of a fitted ellipse around the human body, motion coefficient and silhouette threshold features....
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TL;DR: A comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made to determine the course of its evolution and help new researchers.
Abstract: Vision-based fall detection systems have experienced fast development over the last years To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed Their characterization and classification techniques were analyzed and categorized Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls In addition, there is no evidence of strong connections between the elderly and the communities of researchers
51 citations
Cites methods from "Fall detection using k-nearest neig..."
...It is used for classification purposes in [16,17,48,74] among others....
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...This technique is used in [16,17,92] to complement other static descriptors and introduce the time component....
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...[16] 2016 Foreground extraction through background subtraction (direct comparison)/global characterization K-nearest neighbor (KNN) RGB Chute dataset—no public access at revision time Accuracy Fall 90% No fall 100%...
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TL;DR: This paper presents a novel visual-based fall detection approach by Dual-Channel Feature Integration that divides the fall event into two parts: falling-state and fallen-state, which describes the fall events from dynamic and static perspectives.
Abstract: Falls have caught great harm to the elderly living alone at home. This paper presents a novel visual-based fall detection approach by Dual-Channel Feature Integration. The proposed approach divides the fall event into two parts: falling-state and fallen-state, which describes the fall events from dynamic and static perspectives. Firstly, the object detection model (Yolo) and the human posture detection model (OpenPose) are used for preprocessing to obtain key points and the position information of a human body. Then, a dual-channel sliding window model is designed to extract the dynamic features of the human body (centroid speed, upper limb velocity) and static features (human external ellipse). After that, MLP (Multilayer Perceptron) and Random Forest are applied to classify the dynamic and static feature data separately. Finally, the classification results are combined for fall detection. Experimental results show that the proposed approach achieves an accuracy of 97.33% and 96.91% when tested with UR Fall Detection Dataset and Le2i Fall Detection Dataset.
25 citations
Cites methods from "Fall detection using k-nearest neig..."
...To overcome this problem, Gunale and Mukherji [18] utilized an ellipse to fit the physical characteristics of a person and used KNN (K-NearestNeighbor) for classification....
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...[18] K. G. Gunale and P. Mukherji, ‘‘Fall detection using k-nearest neighbor classification for patient monitoring,’’ in Proc....
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