Literature DB >> 30946684

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

Jose Clemente, Fangyu Li, Maria Valero, WenZhan Song.   

Abstract

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.

Entities:  

Mesh:

Year:  2019        PMID: 30946684     DOI: 10.1109/JBHI.2019.2907498

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  4 in total

1.  Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method.

Authors:  Biao Zhou; Yingbin Gui; Xiaojian Wang; Xiongyao Xie
Journal:  Sensors (Basel)       Date:  2022-04-24       Impact factor: 3.576

2.  Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

Authors:  Osama Shahid; Mohammad Nasajpour; Seyedamin Pouriyeh; Reza M Parizi; Meng Han; Maria Valero; Fangyu Li; Mohammed Aledhari; Quan Z Sheng
Journal:  J Biomed Inform       Date:  2021-03-24       Impact factor: 8.000

3.  Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning.

Authors:  Vera Lobanova; Valeriy Slizov; Lesya Anishchenko
Journal:  Sensors (Basel)       Date:  2022-08-21       Impact factor: 3.847

4.  Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks.

Authors:  Daniel Konings; Fakhrul Alam; Nathaniel Faulkner; Calum de Jong
Journal:  Sensors (Basel)       Date:  2022-09-23       Impact factor: 3.847

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.