Quantitative evaluation of movement using the timed up-and-go test

Higashi, Yuji; Yamakoshi, Ken-ichi; Fujimoto, Toshiro; Shimizu, Masaki; Tamura, Toshiyo

doi:10.1109/memb.2008.919494

Cited by 86 publications

(48 citation statements)

References 11 publications

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“…For fall-risk assessment, an instrumented TUG test that uses portable inertial sensors (iTUG test) has been proposed to improve the TUG test in several ways; namely, by allowing for automatic detection and separation of each performance, facilitating a detailed analysis of each and enabling a higher sensitivity than the TUG test [21,[24][25][26]. Sensitive and reliable measures for fall-risk assessment have been provided by measurement of wearable inertial sensors [27].…”

Section: Discussionmentioning

confidence: 99%

“…A previous study showed that frail elderly subjects exhibited significantly decreased smoothness in the sit-stand transition pattern [8,21,23,[29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

“…Sway and standing balance have been estimated using accelerometry and gyro-sensors. Several other gait performance measures, including the TUG test, have also been evaluated [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Various parameters have been measured and collected to classify the risk of falling among the elderly.…”

Section: Introductionmentioning

confidence: 99%

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Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Zakaria¹

2014

Int J Neurorehabilitation Eng

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This study aimed to show that important fall risk measures among the elderly can be classified using multiple parameters obtained from wearable inertial sensors. The timed up-and-go (TUG) test, a well-known standard assessment test, was used to evaluate the risk of falling among elderly individuals. The use of wearable inertial sensors enables extraction of triaxial acceleration and angular velocity signals for offline analysis. Thirty-eight elderly patients from Fujimoto Hayasuzu Hospital participated in this study. Specific results were provided from the signals obtained from acceleration and angular velocity, and analysis was carried out in each phase of various activities, such as sit-to-stand, walking, etc. Seventy-eight parameters were obtained from the extracted acceleration and angular velocity signals in all phases to classify the risk of falling among the elderly. Using principle component analysis, the most important measures were selected from the gathered parameters. The most influential measure in differentiating subjects with high and low fall risks was the turning angular velocity signal. Methods SubjectsThirty-eight elderly subjects (male, 20; female, 18) with an average age of 65.18 ± 8.90 years from Fujimoto Hayasuzu Hospital, Japan participated in the TUG test. The low-fall-risk (LFR) group comprised 27 subjects, and the high-fall-risk (HFR) group comprised 11 subjects.

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Section: Discussionmentioning

confidence: 99%

“…A previous study showed that frail elderly subjects exhibited significantly decreased smoothness in the sit-stand transition pattern [8,21,23,[29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

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Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Zakaria¹

2014

Int J Neurorehabilitation Eng

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“…Several studies use wearable inertial sensors to detect the phases in TUG test by using the Euler angles or angular velocity signals that had been processed by filters [25], [26]. Some specific algorithms used to filter out the noise of the sensors for the detection are presented in [27], [46].…”

Section: Detection Of Distinct Phasesmentioning

confidence: 99%

“…Another drawback of most studies is that these measures are obtained on a reduced space with specialized laboratory equipment such as motion capture systems and instrumented walkways, which may not be available in many clinics and certainly not during daily activities. Thus the recent studies used the wearable inertial sensors to analyze the fall risk of elderly people [25], [26], [27]. Thanks to encoders and a 3D accelerometer/gyrometer, by using the smart walker we can not only calculate accurately all the classical gait parameters proposed above, but also avoid imposing constraints on the end-users.…”

Section: Introductionmentioning

confidence: 99%

A New Application of Smart Walker for Quantitative Analysis of Human Walking

Wang

Dune

Merlet

et al. 2015

Computer Vision - ECCV 2014 Workshops

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Abstract. This paper presents a new nonintrusive device for everyday gait analysis and health monitoring. The system is a standard rollator equipped with encoders and inertial sensors. The assisted walking of 25 healthy elderly and 23 young adults are compared to develop walking quality index. The subjects were asked to walk on a straight trajectory and an L-shaped trajectory respectively. The walking trajectory, which is missing in other gait analysis methods, is calculated based on the encoder data. The obtained trajectory and steps are compared with the results of a motion capture system. The gait analysis results show that new index obtained by using the walker measurements, and not available otherwise, are very discriminating, e.g., the elderly have larger lateral motion and maneuver area, smaller angular velocity during turning, their walking accuracy is lower and turning ability is weaker although they have almost the same walking velocity as the young people.

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