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

FADES: Behavioral detection of falls using body shapes from 3D joint data
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]
Issued Date
Article Type
CamerasClassification (of Information)Classification MethodsClassification SystemFall DetectionHome AssistanceKinect Skeletal Joint DataReal-Time ProcessingReal Time PerformanceSkeletal JointsState-of-the-Art TechniquesSupport Vector MachinesViterbi Algorithm
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.
IOS Press
Related Researcher
  • 손상혁 Son, Sang Hyuk 정보통신융합전공
  • Research Interests Real-time system; Wireless sensor network; Cyber-physical system; Data and event service; Information security; 실시간 임베디드 시스템
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Department of Electrical Engineering and Computer Science RTCPS(Real-Time Cyber-Physical Systems) Lab 1. Journal Articles


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