Literature DB >> 20525533

Detection of falls among the elderly by a floor sensor using the electric near field.

Henry Rimminen1, Juha Lindström, Matti Linnavuo, Raimo Sepponen.   

Abstract

We present a new fall-detection method using a floor sensor based on near-field imaging. The test floor had a resolution of 9 × 16. The shape, size, and magnitude of the patterns are used for classification. A test including 650 events and ten people yielded a sensitivity of 91% and a specificity of 91%.

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Mesh:

Year:  2010        PMID: 20525533     DOI: 10.1109/TITB.2010.2051956

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  18 in total

1.  Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

Authors:  Satya Samyukta Kambhampati; Vishal Singh; M Sabarimalai Manikandan; Barathram Ramkumar
Journal:  Healthc Technol Lett       Date:  2015-08-03

Review 2.  Aging society and gerontechnology: a solution for an independent living?

Authors:  A Piau; E Campo; P Rumeau; B Vellas; F Nourhashémi
Journal:  J Nutr Health Aging       Date:  2014-01       Impact factor: 4.075

Review 3.  The Elderly's Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development.

Authors:  Qin Ni; Ana Belén García Hernando; Iván Pau de la Cruz
Journal:  Sensors (Basel)       Date:  2015-05-14       Impact factor: 3.576

Review 4.  Fall detection devices and their use with older adults: a systematic review.

Authors:  Shomir Chaudhuri; Hilaire Thompson; George Demiris
Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

5.  Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

Authors:  Nabil Zerrouki; Fouzi Harrou; Ying Sun; Amrane Houacine
Journal:  J Med Syst       Date:  2016-10-29       Impact factor: 4.460

6.  A machine learning based sentient multimedia framework to increase safety at work.

Authors:  Gianluca Bonifazi; Enrico Corradini; Domenico Ursino; Luca Virgili; Emiliano Anceschi; Massimo Callisto De Donato
Journal:  Multimed Tools Appl       Date:  2021-05-15       Impact factor: 2.757

Review 7.  Challenges, issues and trends in fall detection systems.

Authors:  Raul Igual; Carlos Medrano; Inmaculada Plaza
Journal:  Biomed Eng Online       Date:  2013-07-06       Impact factor: 2.819

Review 8.  Automatic fall monitoring: a review.

Authors:  Natthapon Pannurat; Surapa Thiemjarus; Ekawit Nantajeewarawat
Journal:  Sensors (Basel)       Date:  2014-07-18       Impact factor: 3.576

Review 9.  REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.

Authors:  Maryam Pishgar; Salah Fuad Issa; Margaret Sietsema; Preethi Pratap; Houshang Darabi
Journal:  Int J Environ Res Public Health       Date:  2021-06-22       Impact factor: 3.390

10.  Detecting falls with wearable sensors using machine learning techniques.

Authors:  Ahmet Turan Özdemir; Billur Barshan
Journal:  Sensors (Basel)       Date:  2014-06-18       Impact factor: 3.576

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