Literature DB >> 15137565

Long-term mobility monitoring of older adults using accelerometers in a clinical environment.

K M Culhane1, G M Lyons, D Hilton, P A Grace, D Lyons.   

Abstract

OBJECTIVE: To assess the accuracy of accelerometer-based mobility monitoring during extended measurements on older adults in a clinical setting and to evaluate two different approaches to thresholding.
DESIGN: The monitoring device consisted of two Analog Devices ADXL202 accelerometers, an ambulatory data-logger and associated cabling. The monitoring system used custom-designed analysis software to detect activities of daily living, namely duration of sitting, standing, lying and moving during the period monitored. An investigator shadowed the subjects throughout the recording period. SUBJECTS AND
SETTING: This study monitored five older adults, with varying degrees of mobility, resident in a rehabilitation clinic, over four days.
INTERVENTIONS: The accelerometer data were analysed using a MATLAB program that allowed trunk and thigh threshold angles to be set to distinguish between sitting, standing, lying and moving. Two different approaches to setting these thresholds were investigated: (1) using a midpoint tolerance value of 45 degrees and (2) using a 'best estimate' tolerance value. The analysis program generates a summary of activities, which is then compared line-by-line with the manual summary created by the observer. The result was a hit/miss ratio representative of the system's accuracy.
RESULTS: The detection accuracies for sitting and lying using a mid-point tolerance value were poor, with an average detection accuracy of 75% obtained. The 'best estimate' approach improved the detection accuracies for sitting and lying by approximately 18% to an average value of 93%.
CONCLUSION: In a population of older adults, the static activities of sitting, standing and lying and dynamic activities can be distinguished using the technique and threshold values outlined here to a degree of accuracy of 92% and higher.

Mesh:

Year:  2004        PMID: 15137565     DOI: 10.1191/0269215504cr734oa

Source DB:  PubMed          Journal:  Clin Rehabil        ISSN: 0269-2155            Impact factor:   3.477


  23 in total

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6.  Evaluation of accelerometer-based fall detection algorithms on real-world falls.

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8.  Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors.

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9.  Methods of Measurement in epidemiology: sedentary Behaviour.

Authors:  Andrew J Atkin; Trish Gorely; Stacy A Clemes; Thomas Yates; Charlotte Edwardson; Soren Brage; Jo Salmon; Simon J Marshall; Stuart J H Biddle
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10.  Recommendations for standardizing validation procedures assessing physical activity of older persons by monitoring body postures and movements.

Authors:  Ulrich Lindemann; Wiebren Zijlstra; Kamiar Aminian; Sebastien F M Chastin; Eling D de Bruin; Jorunn L Helbostad; Johannes B J Bussmann
Journal:  Sensors (Basel)       Date:  2014-01-10       Impact factor: 3.576

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