Literature DB >> 23184296

Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors.

C Becker1, L Schwickert, S Mellone, F Bagalà, L Chiari, J L Helbostad, W Zijlstra, K Aminian, A Bourke, C Todd, S Bandinelli, N Kerse, J Klenk.   

Abstract

Falls are by far the leading cause of fractures and accidents in the home environment. The current Cochrane reviews and other systematic reviews report on more than 200 intervention studies about fall prevention. A recent meta-analysis has summarized the most important risk factors of accidental falls. However, falls and fall-related injuries remain a major challenge. One novel approach to recognize, analyze, and work better toward preventing falls could be the differentiation of the fall event into separate phases. This might aid in reconsidering ways to design preventive efforts and diagnostic approaches. From a conceptual point of view, falls can be separated into a pre-fall phase, a falling phase, an impact phase, a resting phase, and a recovery phase. Patient and external observers are often unable to give detailed comments concerning these phases. With new technological developments, it is now at least partly possible to examine the phases of falls separately and to generate new hypotheses.The article describes the practicality and the limitations of this approach using body-fixed sensor technology. The features of the different phases are outlined with selected real-world fall signals.

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Year:  2012        PMID: 23184296     DOI: 10.1007/s00391-012-0403-6

Source DB:  PubMed          Journal:  Z Gerontol Geriatr        ISSN: 0948-6704            Impact factor:   1.281


  25 in total

Review 1.  Systematic review of definitions and methods of measuring falls in randomised controlled fall prevention trials.

Authors:  Klaus Hauer; Sarah E Lamb; Ellen C Jorstad; Chris Todd; Clemens Becker
Journal:  Age Ageing       Date:  2006-01       Impact factor: 10.668

2.  Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.

Authors:  A K Bourke; J V O'Brien; G M Lyons
Journal:  Gait Posture       Date:  2006-11-13       Impact factor: 2.840

3.  Martial arts fall techniques decrease the impact forces at the hip during sideways falling.

Authors:  B E Groen; V Weerdesteyn; J Duysens
Journal:  J Biomech       Date:  2006-02-09       Impact factor: 2.712

4.  Falls in older people.

Authors:  A John Campbell
Journal:  BMJ       Date:  2008-11-25

5.  Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus.

Authors:  Sarah E Lamb; Ellen C Jørstad-Stein; Klaus Hauer; Clemens Becker
Journal:  J Am Geriatr Soc       Date:  2005-09       Impact factor: 5.562

6.  A hypothesis: the causes of hip fractures.

Authors:  S R Cummings; M C Nevitt
Journal:  J Gerontol       Date:  1989-07

7.  Majority of hip fractures occur as a result of a fall and impact on the greater trochanter of the femur: a prospective controlled hip fracture study with 206 consecutive patients.

Authors:  J Parkkari; P Kannus; M Palvanen; A Natri; J Vainio; H Aho; I Vuori; M Järvinen
Journal:  Calcif Tissue Int       Date:  1999-09       Impact factor: 4.333

Review 8.  Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis.

Authors:  Silvia Deandrea; Ersilia Lucenteforte; Francesca Bravi; Roberto Foschi; Carlo La Vecchia; Eva Negri
Journal:  Epidemiology       Date:  2010-09       Impact factor: 4.822

Review 9.  Interventions for preventing falls in older people living in the community.

Authors:  Lesley D Gillespie; M Clare Robertson; William J Gillespie; Catherine Sherrington; Simon Gates; Lindy M Clemson; Sarah E Lamb
Journal:  Cochrane Database Syst Rev       Date:  2012-09-12

10.  Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90.

Authors:  Jane Fleming; Carol Brayne
Journal:  BMJ       Date:  2008-11-17
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  17 in total

Review 1.  Fall detection with body-worn sensors : a systematic review.

Authors:  L Schwickert; C Becker; U Lindemann; C Maréchal; A Bourke; L Chiari; J L Helbostad; W Zijlstra; K Aminian; C Todd; S Bandinelli; J Klenk
Journal:  Z Gerontol Geriatr       Date:  2013-12       Impact factor: 1.281

2.  Conflicting Aims and Values in the Application of Smart Sensors in Geriatric Rehabilitation: Ethical Analysis.

Authors:  Christopher Predel; Cristian Timmermann; Frank Ursin; Marcin Orzechowski; Timo Ropinski; Florian Steger
Journal:  JMIR Mhealth Uhealth       Date:  2022-06-23       Impact factor: 4.947

3.  Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor.

Authors:  Hagar Bernad-Elazari; Talia Herman; Anat Mirelman; Eran Gazit; Nir Giladi; Jeffrey M Hausdorff
Journal:  J Neurol       Date:  2016-05-23       Impact factor: 4.849

4.  A wavelet-based approach to fall detection.

Authors:  Luca Palmerini; Fabio Bagalà; Andrea Zanetti; Jochen Klenk; Clemens Becker; Angelo Cappello
Journal:  Sensors (Basel)       Date:  2015-05-20       Impact factor: 3.576

5.  An ecologically-controlled exoskeleton can improve balance recovery after slippage.

Authors:  V Monaco; P Tropea; F Aprigliano; D Martelli; A Parri; M Cortese; R Molino-Lova; N Vitiello; S Micera
Journal:  Sci Rep       Date:  2017-05-11       Impact factor: 4.379

6.  An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

Authors:  Jian He; Shuang Bai; Xiaoyi Wang
Journal:  Sensors (Basel)       Date:  2017-06-16       Impact factor: 3.576

7.  Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.

Authors:  Goran Šeketa; Lovro Pavlaković; Dominik Džaja; Igor Lacković; Ratko Magjarević
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

Review 8.  Analysis of Android Device-Based Solutions for Fall Detection.

Authors:  Eduardo Casilari; Rafael Luque; María-José Morón
Journal:  Sensors (Basel)       Date:  2015-07-23       Impact factor: 3.576

Review 9.  Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement.

Authors:  Michael B del Rosario; Stephen J Redmond; Nigel H Lovell
Journal:  Sensors (Basel)       Date:  2015-07-31       Impact factor: 3.576

10.  The Discriminant Value of Phase-Dependent Local Dynamic Stability of Daily Life Walking in Older Adult Community-Dwelling Fallers and Nonfallers.

Authors:  Espen A F Ihlen; Aner Weiss; Jorunn L Helbostad; Jeffrey M Hausdorff
Journal:  Biomed Res Int       Date:  2015-09-30       Impact factor: 3.411

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