Literature DB >> 35161731

Fall Risk Assessment Using Wearable Sensors: A Narrative Review.

Rafael N Ferreira1,2,3, Nuno Ferrete Ribeiro1,2,3, Cristina P Santos1,2,3.   

Abstract

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

Entities:  

Keywords:  fall prediction; fall risk assessment; wearable sensors

Mesh:

Year:  2022        PMID: 35161731      PMCID: PMC8838304          DOI: 10.3390/s22030984

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  42 in total

Review 1.  Monitoring falls in cohort studies of community-dwelling older people: effect of the recall interval.

Authors:  David A Ganz; Takahiro Higashi; Laurence Z Rubenstein
Journal:  J Am Geriatr Soc       Date:  2005-12       Impact factor: 5.562

2.  The frequency content of gait.

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Journal:  J Biomech       Date:  1985       Impact factor: 2.712

3.  A 30-s chair-stand test as a measure of lower body strength in community-residing older adults.

Authors:  C J Jones; R E Rikli; W C Beam
Journal:  Res Q Exerc Sport       Date:  1999-06       Impact factor: 2.500

4.  Measuring balance in the elderly: validation of an instrument.

Authors:  K O Berg; S L Wood-Dauphinee; J I Williams; B Maki
Journal:  Can J Public Health       Date:  1992 Jul-Aug

5.  Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study.

Authors:  Kim Delbaere; Geert Crombez; Guy Vanderstraeten; Tine Willems; Dirk Cambier
Journal:  Age Ageing       Date:  2004-03-26       Impact factor: 10.668

Review 6.  Perturbation-based balance training for falls reduction among older adults: Current evidence and implications for clinical practice.

Authors:  Marissa H G Gerards; Christopher McCrum; Avril Mansfield; Kenneth Meijer
Journal:  Geriatr Gerontol Int       Date:  2017-06-16       Impact factor: 2.730

7.  Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People.

Authors:  Hai Qiu; Rana Zia Ur Rehman; Xiaoqun Yu; Shuping Xiong
Journal:  Sci Rep       Date:  2018-11-05       Impact factor: 4.379

8.  Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors.

Authors:  Barry R Greene; Killian McManus; Stephen J Redmond; Brian Caulfield; Charlene C Quinn
Journal:  NPJ Digit Med       Date:  2019-12-11

Review 9.  Review of fall risk assessment in geriatric populations using inertial sensors.

Authors:  Jennifer Howcroft; Jonathan Kofman; Edward D Lemaire
Journal:  J Neuroeng Rehabil       Date:  2013-08-08       Impact factor: 4.262

Review 10.  Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Authors:  Ramesh Rajagopalan; Irene Litvan; Tzyy-Ping Jung
Journal:  Sensors (Basel)       Date:  2017-11-01       Impact factor: 3.576

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  1 in total

Review 1.  Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall.

Authors:  Ioannis Bargiotas; Danping Wang; Juan Mantilla; Flavien Quijoux; Albane Moreau; Catherine Vidal; Remi Barrois; Alice Nicolai; Julien Audiffren; Christophe Labourdette; François Bertin-Hugaul; Laurent Oudre; Stephane Buffat; Alain Yelnik; Damien Ricard; Nicolas Vayatis; Pierre-Paul Vidal
Journal:  J Neurol       Date:  2022-07-11       Impact factor: 6.682

  1 in total

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