Portable preimpact fall detector with inertial sensors
Abstract
Falls and the resulting hip fractures in the elderly are a major health and economic problem. The goal of this study was to investigate the feasibility of a portable preimpact fall detector in detecting impending falls before the body impacts on the ground. It was hypothesized that a single sensor with the appropriate kinematics measurements and detection algorithms, located near the body center of gravity, would be able to distinguish an in-progress and unrecoverable fall from nonfalling activities. The apparatus was tested in an array of daily nonfall activities of young (n=10) and elderly (n=14) subjects, and simulated fall activities of young subjects. A threshold detection method was used with the magnitude of inertial frame vertical velocity as the main variable to separate the nonfall and fall activities. The algorithm was able to detect all fall events at least 70 ms before the impact. With the threshold adapted to each individual subject, all falls were detected successfully, and no false alarms occurred. This portable preimpact fall detection apparatus will lead to the development of a new generation inflatable hip pad for preventing fall-related hip fractures.
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Body-worn gyroscopes measuring angular velocity, sensors to measure the acceleration of the trunk, and inertial sensors may detect when a fall occurs, along with the causes of the fall.
“Body-worn gyroscopes measuring angular velocity, sensors to measure the acceleration of the trunk, and inertial sensors may detect when a fall occurs, along with the causes of the fall (276–279).”
From The Multidisciplinary Approach to Alzheimer’s Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment (2018) · cites this paper as [278]
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- Authors
- Wu, G., & Xue, S.
- Affiliations
- University of Vermont (US)
- ORCID
- Wu, Ge
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2008-04
- Volume
- 16
- Issue
- 2
- Pages
- 178-183
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2152449912
- Cited by
- 227
- References
- 35
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 13 · 2023: 10 · 2022: 6 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 19 · 2017: 20 · 2016: 19 · 2015: 20 · 2014: 21 · 2013: 18 · 2012: 15
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1r43ag022265-01)
- National Institute on Aging (1R43AG022265-01)
- 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, inertial sensors, hip fracture prevention, falls in older adults, wearable sensors, fall-related injuries
- MeSH terms
- Acceleration, Accidental Falls, Adult, Aged, Aged, 80 and over, Algorithms, Equipment Design, Feasibility Studies, Female, Hip Fractures, Humans, Male, Pattern Recognition, Automated, Protective Clothing, Sensitivity and Specificity, Transducers, Reproducibility of Results, Monitoring, Ambulatory, Equipment Failure Analysis
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