Fall Detection Devices and Their Use With Older Adults: A Systematic Review
Open accessAbstract
BACKGROUND: Falls represent a significant threat to the health and independence of adults aged 65 years and older. As a wide variety and large number of passive monitoring systems are currently and increasingly available to detect when individuals have fallen, there is a need to analyze and synthesize the evidence regarding their ability to accurately detect falls to determine which systems are most effective. OBJECTIVES: The purpose of this literature review is to systematically assess the current state of design and implementation of fall-detection devices. This review also examines to what extent these devices have been tested in the real world as well as the acceptability of these devices to older adults. DATA SOURCES: A systematic literature review was conducted in PubMed, CINAHL, EMBASE, and PsycINFO from their respective inception dates to June 25, 2013. STUDY ELIGIBILITY CRITERIA AND INTERVENTIONS: Articles were included if they discussed a project or multiple projects involving a system with the purpose of detecting a fall in adults. It was not a requirement for inclusion in this review that the system targets persons older than 65 years. Articles were excluded if they were not written in English or if they looked at fall risk, fall detection in children, fall prevention, or a personal emergency response device. STUDY APPRAISAL AND SYNTHESIS METHODS: Studies were initially divided into those using sensitivity, specificity, or accuracy in their evaluation methods and those using other methods to evaluate their devices. Studies were further classified into wearable devices and nonwearable devices. Studies were appraised for inclusion of older adults in sample and if evaluation included real-world settings. RESULTS: This review identified 57 projects that used wearable systems and 35 projects using nonwearable systems, regardless of evaluation technique. Nonwearable systems included cameras, motion sensors, microphones, and floor sensors. Of the projects examining wearable systems, only 7.1% reported monitoring older adults in a real-world setting. There were no studies of nonwearable devices that used older adults as subjects in either a laboratory or a real-world setting. In general, older adults appear to be interested in using such devices although they express concerns over privacy and understanding exactly what the device is doing at specific times. LIMITATIONS: This systematic review was limited to articles written in English and did not include gray literature. Manual paper screening and review processes may have been subject to interpretive bias. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: There exists a large body of work describing various fall-detection devices. The challenge in this area is to create highly accurate unobtrusive devices. From this review it appears that the technology is becoming more able to accomplish such a task. There is a need now for more real-world tests as well as standardization of the evaluation of these devices.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Among non-wearable systems, cameras, motion sensors, microphones and floor sensors have been applied to fall monitoring and prevention.
“Among non-wearable systems cameras, motion sensors, microphones and floor sensors have been applied to this aim (280, 281).”
From The Multidisciplinary Approach to Alzheimer’s Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment (2018) · cites this paper as [281]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 126 · Cited by 215
Research works printed but not linked: 14
- [1]Lord SR Sherrington C Menz HB Falls in older people: risk factors and strategies for prevention 2001 Cambridge, U.K Cambridge Univ. Press
- [7]Hausdorff JM Rios DA Edelberg HK Gait variability and fall risk in community-living older adults: a 1-year prospective study Arch Phys Med Rehabil 2001 82 8 1050 1056 11494184
- [29]Sixsmith A Johnson N A smart sensor to detect the falls of the elderly IEEE Pervasive Comput, IEEE 2004 3 2 42 47
- [32]Alwan M Rajendran PJ Kell S A Smart and Passive Floor-Vibration Based Fall Detector for Elderly Vol. 1 Damascus, Syria In: 2nd International Conference on Information and Communication Technologies Apr 24–28 2006
- [78]Noury N Barralon P Virone G Boissy P Hamel M Rumeau P A smart sensor based on rules and its evaluation in daily routines Vol. 4 Cancun, Mexico In: Proceedings of the 25th Annual International Conference of the IEEE-EMBS Sept 17–21, 2003
- [92]Chia-Wen L Zhi-Hong L Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare Honolulu, Hawaii, USA In: Proceedings of the 16th International Conference on Computer on Communications and Networks Aug 13–16, 2007
- [93]Foroughi H Aski BS Pourreza H Intelligent video surveillance for monitoring fall detection of elderly in home environments Khulna, Bangladesh In: Proceedings of the 11th International Conference on Computer and Information Technology Dec 25–27 2008
- [98]Rougier C Meunier J St-Arnaud A Rousseau J Fall Detection from Human Shape and Motion History Using Video Surveillance Vol. 2 Niagara Falls, Canada In: 21st International Conference on Advanced Information Networking and Applications Workshop May 21–23 2007
- [112]Prado M Reina-Tosina J Roa L Distributed intelligent architecture for falling detection and physical activity analysis in the elderly Vol. 3 Houston, Texas, USA Houston, Texas, USA In: Proceedings of the 24th Annual International Conference of the IEEE-EMBS Oct 23–26. 2002
- [114]Mathie MJ Basilakis J Celler BG A system for monitoring posture and physical activity using accelerometers Vol. 4 Istanbul, Turkey In: Proceedings of the 23th Annual International Conference of the IEEE-EMBS Oct 23–26, 2001
- [115]Tamura T Yoshimura T Horiuchi F Higashi Y Fujimoto T An ambulatory fall monitor for the elderly Vol. 4 Chicago, Illinois, USA In: Proceedings of the 22nd International Conference on Engineering in Medicine and Biology Society Jul 23–28, 2000
- [119]Noury N Herve T Rialle V Monitoring behavior in home using a smart fall sensor and position sensors Lyon, France In: 1st International Conference on Microtechnologies in Medicine and Biology Oct 12–14 2000
- [122]Nait-Charif H McKenna SJ Activity summarisation and fall detection in a supportive home environment Vol. 4 Cambridge, England, UK In: 17th International Conference on Pattern Recognition Aug 23–26 2004
- [124]Spher J Gvercin M Winkelbach S Steinhagen-Thiessne E Wahl F Visual fall detection in home environments Vol. 7 No 2 Pisa, Italy In: 6th International Conference of the International Society of Gerontechnology Jun 4–5, 2008
- Authors
- Chaudhuri, S., Thompson, H., & Demiris, G.
- Affiliations
- University of Washington (US)
- Corresponding author
- Chaudhuri, Shomir
- Venue
- Journal of Geriatric Physical Therapy
- Published
- 2014-10
- Volume
- 37
- Issue
- 4
- Pages
- 178-196
- ISSN
- 1539-8412 (ISSN-L)
- PMC
- PMC4087103
- OpenAlex
- W2140943894
- Cited by
- 215
- References
- 126
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 14 · 2023: 9 · 2022: 14 · 2021: 20 · 2020: 34 · 2019: 22 · 2018: 22 · 2017: 22 · 2016: 15 · 2015: 5 · 2014: 9
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Geriatric Physical Therapy (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. National Library of Medicine (T15LM007442)
- 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
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- fall detection, older adults, wearable devices, real-world testing, device accuracy, acceptability, privacy concerns, motion sensors, floor sensors, camera-based detection, non-intrusive monitoring
- MeSH terms
- Accidental Falls, Aged, Equipment Design, Humans, Sensitivity and Specificity, Telemedicine, Monitoring, Ambulatory
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 ›
Are you an author, or do you see a problem with this page? Request a review