Cited 1 time in webofscience Cited 3 time in scopus

FADES: Behavioral detection of falls using body shapes from 3D joint data

Title
FADES: Behavioral detection of falls using body shapes from 3D joint data
Authors
Yoon, HJ[Yoon, Hee Jung]Ra, HK[Ra, Ho-Kyeong]Park, T[Park, Taejoon]Chung, S[Chung, Sam]Son, SH[Son, Sang Hyuk]
DGIST Authors
Yoon, HJ[Yoon, Hee Jung]; Ra, HK[Ra, Ho-Kyeong]; Son, SH[Son, Sang Hyuk]
Issue Date
2015
Citation
Journal of Ambient Intelligence and Smart Environments, 7(6), 861-877
Type
Article
Article Type
Article
Keywords
CamerasClassification (of Information)Classification MethodsClassification SystemFall DetectionHome AssistanceKinect Skeletal Joint DataReal-Time ProcessingReal Time PerformanceSkeletal JointsState-of-the-Art TechniquesSupport Vector MachinesViterbi Algorithm
ISSN
1876-1364
Abstract
Many efforts have been made to design classification systems that can aid the protection of elderly in a home environment. In this work, we focus on an accident that is a great risk for seniors living alone, a fall. Specifically, we present FADES, which uses skeletal joint information collected from a 3D depth camera to accurately classify different types of falls facing various directions from a single camera and distinguish an actual fall versus a fall-like activity, even in the presence of partially occluding objects. The framework of FADES is designed using two different phases to classify the detection of a fall, a non-fall, or normal behavior. For the first phase, we use a classification method based on Support Vector Machine (SVM) to detect body shapes that appear during an interval of falling behavior. During the second phase, we aggregate the results of the first phase using a frequency-based method to determine the similarity between the behavior sequences trained for each of the behavior. Our system shows promising results that is comparable to state-of-the-art techniques such as Viterbi algorithm, revealing real time performance with latency of <45 ms and achieving the detection accuracy of 96.07% and 95.7% for falls and non-falls, respectively. © 2015 - IOS Press and the authors. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/2966
DOI
10.3233/AIS-150349
Publisher
IOS Press
Related Researcher
  • Author Son, Sang Hyuk RTCPS(Real-Time Cyber-Physical Systems Research) Lab
  • Research Interests
Files:
There are no files associated with this item.
Collection:
Information and Communication EngineeringRTCPS(Real-Time Cyber-Physical Systems) Lab1. Journal Articles


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