Real-time fall detection based on vision for low-power edge platforms
Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of-Mass (CoM) subsystem and a Base-of-Support (BoS) subsystem, both instantiated as Liquid Time-Consta
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- LinkedLinked via arxiv author · 85%Wenjun Xia →
“Real-time fall detection based on vision for low-power edge platforms”
- LinkedLinked via arxiv author · 85%Zhicheng Peng →
“Real-time fall detection based on vision for low-power edge platforms”
- LinkedLinked via arxiv author · 85%Haopeng Li →
“Real-time fall detection based on vision for low-power edge platforms”
- LinkedLinked via arxiv author · 85%Zhengdi Zhang →
“Real-time fall detection based on vision for low-power edge platforms”