Fall detection with body-worn sensors: A systematic review
Abstract
Background and aims Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics).Methods The searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96 records (33 journal articles, 60 conference proceedings and 3 project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA).Results The main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies.Conclusion Limited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition.
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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 [279]
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Salomon, R., Lüder, M., & Bieber, G. (2010) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2010.5637542 · Cited by 3 · see citing works
A fall and loss of consciousness wearable detector
Quagliarella, L., Sasanelli, N., & Belgiovine, B. (2008) · Gerontechnology · doi:10.4017/gt.2008.07.02.128.00 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zucchella, C., Sinforiani, E., Tamburin, S., Federico, A., Mantovani, E., Bernini, S., Casale, R., & Bartolo, M. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.01058 · Cited by 297 · see citing works
Yu, L., Xiong, D., Guo, L., & Wang, J. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.02.012 · Cited by 134 · see citing works
A comparison of public datasets for acceleration-based fall detection
Igual, R., Medrano, C., & Plaza, I. (2015) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.06.009 · Cited by 98 · see citing works
- Authors
- for the FARSEEING Consortium and the FARSEEING Meta Database Consensus Group, Schwickert, L., Becker, C., Lindemann, U., Maréchal, C., Bourke, A., Chiari, L., Helbostad, J. L., Zijlstra, W., Aminian, K., Todd, C., Bandinelli, S., & Klenk, J.
- Affiliations
- Robert Bosch Hospital (DE)
- École Polytechnique Fédérale de Lausanne (CH)
- University of Bologna (IT)
- Norwegian University of Science and Technology (NO)
- German Sport University Cologne (DE)
- University of Manchester (GB)
- Azienda Sanitaria di Firenze (IT)
- Universität Ulm (DE)
- Corresponding author
- Becker, C.
- Klenk, J.
- ORCID
- Venue
- Zeitschrift für Gerontologie und Geriatrie
- Published
- 2013-11-24
- Volume
- 46
- Issue
- 8
- Pages
- 706-719
- ISSN
- 0948-6704 (ISSN-L)
- 1435-1269
- OpenAlex
- W2127108064
- Cited by
- 148
- References
- 107
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 2 · 2023: 7 · 2022: 15 · 2021: 6 · 2020: 16 · 2019: 9 · 2018: 14 · 2017: 20 · 2016: 18 · 2015: 18 · 2014: 11 · 2013: 1
- FWCI
- 41.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Zeitschrift für Gerontologie und Geriatrie (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- fall detection, wearable sensors, falls in older adults, methodological agreement
- MeSH terms
- Accidental Falls, Humans, Transducers, Telemedicine, Monitoring, Ambulatory, Evidence-Based Medicine, Actigraphy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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