Literature DB >> 33494509

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole.

Johannes C Ayena1, Lydia Chioukh1, Martin J-D Otis2, Dominic Deslandes1.   

Abstract

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.

Entities:  

Keywords:  TUG; UWB radar; biomedical monitoring; fall detection; gait parameters; non-contact

Mesh:

Year:  2021        PMID: 33494509      PMCID: PMC7866057          DOI: 10.3390/s21030722

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


  37 in total

1.  Quantitative falls risk assessment using the timed up and go test.

Authors:  Barry R Greene; Alan O'Donovan; Roman Romero-Ortuno; Lisa Cogan; Cliodhna Ni Scanaill; Rose A Kenny
Journal:  IEEE Trans Biomed Eng       Date:  2010-10-04       Impact factor: 4.538

2.  iGAIT: an interactive accelerometer based gait analysis system.

Authors:  Mingjing Yang; Huiru Zheng; Haiying Wang; Sally McClean; Dave Newell
Journal:  Comput Methods Programs Biomed       Date:  2012-05-08       Impact factor: 5.428

3.  Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

Authors:  Julius Hannink; Thomas Kautz; Cristian F Pasluosta; Jens Barth; Samuel Schulein; Karl-Gunter GaBmann; Jochen Klucken; Bjoern M Eskofier
Journal:  IEEE J Biomed Health Inform       Date:  2017-03-09       Impact factor: 5.772

Review 4.  Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review.

Authors:  Shanshan Chen; John Lach; Benny Lo; Guang-Zhong Yang
Journal:  IEEE J Biomed Health Inform       Date:  2016-11       Impact factor: 5.772

5.  Evaluation of the turning characteristics according to the severity of Parkinson disease during the timed up and go test.

Authors:  Minji Son; Changhong Youm; Sangmyung Cheon; Jaewoo Kim; Meounggon Lee; Youkyung Kim; Jinhee Kim; Hyeryun Sung
Journal:  Aging Clin Exp Res       Date:  2017-02-20       Impact factor: 3.636

6.  Assessing mobility at home in people with early Parkinson's disease using an instrumented Timed Up and Go test.

Authors:  Cris Zampieri; Arash Salarian; Patricia Carlson-Kuhta; John G Nutt; Fay B Horak
Journal:  Parkinsonism Relat Disord       Date:  2010-08-30       Impact factor: 4.891

Review 7.  A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications.

Authors:  Delaram Jarchi; James Pope; Tracey K M Lee; Larisa Tamjidi; Amirhosein Mirzaei; Saeid Sanei
Journal:  IEEE Rev Biomed Eng       Date:  2018-02-16

8.  Association between Gait Deviation Index and Physical Function in Children with Bilateral Spastic Cerebral Palsy: A Cross-Sectional Study.

Authors:  Tadashi Ito; Koji Noritake; Hiroshi Sugiura; Yasunari Kamiya; Hidehito Tomita; Yuji Ito; Hideshi Sugiura; Nobuhiko Ochi; Yuji Yoshihashi
Journal:  J Clin Med       Date:  2019-12-20       Impact factor: 4.241

9.  The Gait Deviation Index Is Associated with Hip Muscle Strength and Patient-Reported Outcome in Patients with Severe Hip Osteoarthritis-A Cross-Sectional Study.

Authors:  Signe Rosenlund; Anders Holsgaard-Larsen; Søren Overgaard; Carsten Jensen
Journal:  PLoS One       Date:  2016-04-11       Impact factor: 3.240

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  2 in total

Review 1.  Wearable Sensor Systems for Fall Risk Assessment: A Review.

Authors:  Sophini Subramaniam; Abu Ilius Faisal; M Jamal Deen
Journal:  Front Digit Health       Date:  2022-07-14

2.  Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.

Authors:  Yoonjeong Choi; Yoosung Bae; Baekdong Cha; Jeha Ryu
Journal:  Sensors (Basel)       Date:  2022-08-23       Impact factor: 3.847

  2 in total

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