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Cited by 296Context-Aware Activity Recognition SystemsBalance, Gait, and Falls PreventionGait Recognition and Analysis

Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection

Bianchi, Federico, Redmond, Stephen J., Narayanan, Michael R., Cerutti, Sergio, Lovell, Nigel H.·2010·IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI 10.1109/tnsre.2010.2070807
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Abstract

Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.

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  • Bianchi et al's barometric pressure sensor method demonstrated considerable improvements in sensitivity and specificity compared to an existing accelerometer-based technique when tested in 20 young healthy volunteers.

    “When tested in a cohort of 20 young healthy volunteers, the proposed method demonstrated considerable improvements in sensitivity and specificity compared to an existing accelerometer-based technique.”

    From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [75]

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Authors
Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., & Lovell, N. H.
Affiliations
  • Politecnico di Milano (IT)
  • UNSW Sydney (AU)
ORCID
Venue
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2010-12
Volume
18
Issue
6
Pages
619-627
ISSN
  • 1534-4320 (ISSN-L)
  • 1558-0210
DOI
10.1109/tnsre.2010.2070807
OpenAlex
W2128628418
Cited by
296
References
24
Citations by year
2026: 1 · 2025: 9 · 2024: 7 · 2023: 9 · 2022: 13 · 2021: 16 · 2020: 25 · 2019: 26 · 2018: 30 · 2017: 32 · 2016: 28 · 2015: 35 · 2014: 23 · 2013: 22 · 2012: 13
FWCI
15.59
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
Keywords
fall detection, barometric pressure sensor, triaxial accelerometry, wearable sensors, activities of daily living, false positive reduction, decision tree classification, falls in older adults, altitude measurement, simulated falls
MeSH terms
Acceleration, Accidental Falls, Activities of Daily Living, Air Pressure, Algorithms, Analog-Digital Conversion, Electronic Data Processing, Decision Trees, Equipment Design, False Negative Reactions, False Positive Reactions, Female, Humans, Male, Monitoring, Ambulatory, Young Adult

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