Literature DB >> 23964848

Quantitative analysis of fall risk using TUG test.

Nor Aini Zakaria1, Yutaka Kuwae, Toshiyo Tamura, Kotaro Minato, Shigehiko Kanaya.   

Abstract

We examined falling risk among elderly using a wearable inertial sensor, which combines accelerometer and gyrosensors devices, applied during the Timed Up and Go (TUG) test. Subjects were categorised into two groups as low fall risk and high fall risk with 13.5 s duration taken to complete the TUG test as the threshold between them. One sensor was attached at the subject's waist dorsally, while acceleration and gyrosensor signals in three directions were extracted during the test. The analysis was carried out in phases: sit-bend, bend-stand, walking, turning, stand-bend and bend-sit. Comparisons between the two groups showed that time parameters along with root mean square (RMS) value, amplitude and other parameters could reveal the activities in each phase. Classification using RMS value of angular velocity parameters for sit-stand phase, RMS value of acceleration for walking phase and amplitude of angular velocity signal for turning phase along with time parameters suggests that this is an improved method in evaluating fall risk, which promises benefits in terms of improvement of elderly quality of life.

Keywords:  Timed Up and Go; falls; gait; wearable inertial sensor

Mesh:

Year:  2013        PMID: 23964848     DOI: 10.1080/10255842.2013.805211

Source DB:  PubMed          Journal:  Comput Methods Biomech Biomed Engin        ISSN: 1025-5842            Impact factor:   1.763


  18 in total

1.  Role of body-worn movement monitor technology for balance and gait rehabilitation.

Authors:  Fay Horak; Laurie King; Martina Mancini
Journal:  Phys Ther       Date:  2014-12-11

Review 2.  Objective falls-risk prediction using wearable technologies amongst patients with and without neurogenic gait alterations: a narrative review of clinical feasibility.

Authors:  Callum M W Betteridge; Pragadesh Natarajan; R Dineth Fonseka; Daniel Ho; Ralph Mobbs; Wen Jie Choy
Journal:  Mhealth       Date:  2021-10-20

3.  Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test.

Authors:  Danique Vervoort; Nicolas Vuillerme; Nienke Kosse; Tibor Hortobágyi; Claudine J C Lamoth
Journal:  PLoS One       Date:  2016-06-06       Impact factor: 3.240

4.  Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability.

Authors:  Karina Lebel; Hung Nguyen; Christian Duval; Réjean Plamondon; Patrick Boissy
Journal:  Front Bioeng Biotechnol       Date:  2017-08-23

Review 5.  Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges.

Authors:  Aliénor Vienne; Rémi P Barrois; Stéphane Buffat; Damien Ricard; Pierre-Paul Vidal
Journal:  Front Psychol       Date:  2017-05-18

6.  Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test.

Authors:  James Beyea; Chris A McGibbon; Andrew Sexton; Jeremy Noble; Colleen O'Connell
Journal:  Sensors (Basel)       Date:  2017-04-23       Impact factor: 3.576

7.  Effects of Nonpharmacological Interventions on Balance Function in Patients with Osteoporosis or Osteopenia: A Network Meta-Analysis of Randomized Controlled Trials.

Authors:  Lu Zhu; Wenzhong Wu; Ming Chen; Daoming Xu; Huaning Xu; Lanying Liu; Jing Liu; Zequan Zhu
Journal:  Evid Based Complement Alternat Med       Date:  2021-05-04       Impact factor: 2.629

8.  Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.

Authors:  Venous Roshdibenam; Gerald J Jogerst; Nicholas R Butler; Stephen Baek
Journal:  Sensors (Basel)       Date:  2021-05-17       Impact factor: 3.576

Review 9.  Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review.

Authors:  Jelena Bezold; Janina Krell-Roesch; Tobias Eckert; Darko Jekauc; Alexander Woll
Journal:  Eur Rev Aging Phys Act       Date:  2021-07-09       Impact factor: 3.878

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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