Cited 1 time in
Cited 3 time in
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]
- Issue Date
- Journal of Ambient Intelligence and Smart Environments, 7(6), 861-877
- Article Type
- Cameras; Classification (of Information); Classification Methods; Classification System; Fall Detection; Home Assistance; Kinect Skeletal Joint Data; Real-Time Processing; Real Time Performance; Skeletal Joints; State-of-the-Art Techniques; Support Vector Machines; Viterbi 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
Son, Sang Hyuk
RTCPS(Real-Time Cyber-Physical Systems Research) Lab
There are no files associated with this item.
- Information and Communication EngineeringRTCPS(Real-Time Cyber-Physical Systems) Lab1. Journal Articles
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.