Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification

This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps...

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Veröffentlicht in:IEEE journal of biomedical and health informatics 2020-02, Vol.24 (2), p.524-532
Hauptverfasser: Clemente, Jose, Li, Fangyu, Valero, Maria, Song, WenZhan
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creator Clemente, Jose
Li, Fangyu
Valero, Maria
Song, WenZhan
description This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.
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subjects Accidental Falls
Aged
Collaboration
Fall detection
Feature extraction
Floors
Humans
in-network system
Informatics
Intelligent sensors
Monitoring, Ambulatory - instrumentation
person identification
Real time
Real-time systems
seismic sensing
Support vector machines
Vibration
Vibrations
Walking
title Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification
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