Automated Detection of Near Falls: Algorithm Development and Preliminary Results

Overview

Journal BMC Res Notes

Publisher Biomed Central

Specialties Biology
General Medicine

Date 2010 Mar 9

PMID 20205708

Citations 18

Authors

Aner Weiss

Ilan Shimkin

Nir Giladi

Jeffrey M Hausdorff

Affiliations

Soon will be listed here.

Abstract

Background: Falls are a major source of morbidity and mortality among older adults. Unfortunately, self-report is, to a large degree, the gold-standard method for characterizing and quantifying fall frequency. A number of studies have demonstrated that near falls predict falls and that near falls may occur more frequently than falls. These studies suggest that near falls might be an appropriate fall risk measure. However, to date, such investigations have also relied on self-report. The purpose of the present study was to develop a method for automatic detection of near falls, potentially a sensitive, objectivemarker of fall risk and to demonstrate the ability to detect near falls using this approach.

Findings: 15 healthy subjects wore a tri-axial accelerometer on the pelvis as they walked on a treadmill under different conditions. Near falls were induced by placing obstacles on the treadmill and were defined using observational analysis. Acceleration-derived parameters were examined as potential indicators of near falls, alone and in various combinations. 21 near falls were observed and compared to 668 "non-near falls" segments, consisting of normal and abnormal (but not near falls) gait. The best single method was based on the maximum peak-to-peak vertical acceleration derivative, with detection rates better than 85% sensitivity and specificity.

Conclusions: These findings suggest that tri-axial accelerometers may be used to successfully distinguish near falls from other gait patterns observed in the gait laboratory and may have the potential for improving the objective evaluation of fall risk, perhaps both in the lab and in at home-settings.

Citing Articles

Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations.

Feld L, Schell-Majoor L, Hellmers S, Koschate J, Hein A, Zieschang T PLOS Digit Health. 2024; 3(8):e0000553.

PMID: 39213262 PMC: 11364241. DOI: 10.1371/journal.pdig.0000553.

Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.

Wang S, Miranda F, Wang Y, Rasheed R, Bhatt T Sensors (Basel). 2022; 22(9).

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Abnormal Gait Movements Prior to a Near Fall in Individuals After Stroke.

Osada Y, Motojima N, Kobayashi Y, Yamamoto S Arch Rehabil Res Clin Transl. 2022; 3(4):100156.

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The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait.

den Hartog D, Harlaar J, Smit G Sensors (Basel). 2021; 21(19).

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Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units.

Hauth J, Jabri S, Kamran F, Feleke E, Nigusie K, Ojeda L Sensors (Basel). 2021; 21(14).

PMID: 34300399 PMC: 8309544. DOI: 10.3390/s21144661.

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